***THIS IS AN EQUITY-ONLY BASED POSITION CURRENTLY***
Company Overview:
Flow Global Software Technologies, LLC. is a next-generation, high-tech, data-native, artificial intelligence, and super advanced exotic technologies company operating within the broader Information Technology sector and Software industry. Flow engages in the architecture, design, engineering, marketing, sales, and blue chip service of bleeding-edge, cloud-based AI sales platforms, Flow Nebula™, Flow Turbo™, and Flow Black Rhino™ LLM, with advanced data infrastructure, data science, data engineering, artificial intelligence, deep learning, and other proprietary technologies. Flow is exclusively focused on the development, deployment, and commercialization of frontier AI systems spanning massive scale data science, cross-domain artificial intelligence, and applied artificial intelligence systems. The company is strategically aligned within the emerging Artificial Intelligence industry, comparable in focus and structure to pioneers like IBM, and OpenAI. Flow is purpose-built to lead in the standalone sovereign-grade data infrastructure and advanced AI systems vertical. The company also provides world-class award-winning customer support, professional services, and advisory services. The company is headquartered in the United States and is registered in Delaware.
The core of Flow’s work is advanced engineering in data science, data engineering, artificial intelligence, deep learning, and large data systems design. The company focuses in the development of bleeding-edge data infrastructure and AI platforms for B2B sales success, with a focus on scalable, high-performance, and sovereign-grade infrastructure. This includes robust back-end architecture, well-defined microservices patterns, and modern data-driven design principles applied across every engineering layer.
Flow's AI architecture leverages state-of-the-art advancements in deep learning, advanced LLM-based workflows, transformer networks, and techniques in natural language processing and understanding (NLP/NLU). The engineering organization builds on semantic modeling principles, incorporating graph theory, vector databases, and graph databases to create structured, high-context reasoning systems. These components are connected together through clearly defined data flows and schema designs that are enforced through precise database ER modeling.
The engineering organization applies a modular, event-driven approach using Apache Kafka to support a distributed microservices environment. This architecture allows for high-throughput messaging, real-time data handling, and fault-tolerant service interactions. Data propagation and event orchestration are controlled through well-defined Kafka topics and message contracts to ensure consistency across the platform.
In terms of implementation, Flow utilizes a polyglot technology stack. Languages such as Rust, Golang, Python, FastAPI, JS, Node.js, and C++ are selected based on platform-specific requirements related to performance, concurrency, and system-level control. FastAPI and gRPC are used for API exposure and inter-service communication, enabling low-latency, high-reliability operations across distributed components.
Security, observability, and reliability are embedded throughout the engineering process. Code is structured for maintainability and correctness, with strong emphasis on static typing, performance profiling, and automated testing pipelines. Proprietary technologies are developed with rigorous documentation, review protocols, and architecture validation processes.
Flow Global Software Technologies, LLC. maintains a focused engineering discipline, and a highly technically intense engineering culture dedicated to advancing sovereign-grade data infrastructure, advanced artificial intelligence, and future-generation AI platforms for B2B sales success without compromise.
Position Overview:
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Flow is seeking deeply technical, superhuman, highly senior Founding Engineers, with strong offensive ideologies, a warrior mentality, and are true data believers, and operate within Tier-0 covert deep black ops engineering hitman teams in ultra-deniable non-kinetic operations, black bag jobs, and non-attributed deep penetration missions. This is a remote-native position for top-tier engineering operators with 8-12+ years of professional industry experience building sovereign-grade data infrastructure and AI systems at the absolute highest level of execution. This role is designed for a systems-first, infrastructure-heavy data engineers who has shipped multiple complex large scale AI systems across the full stack—from AI to backend to deployment pipelines—in extremely demanding production environments. The Tier-0 engineering culture is 100% trust, 100% dependability, 100% reliability, 100% full autonomy, and expects a Tier-0 most elite level of technical ownership.
This Tier-0 position demands founding-level engineering architects whose capabilities align with those expected of a post-doctoral data science researcher or principal-level systems scientist working at the frontier of advanced data science, data engineering, distributed systems, advanced artificial intelligence, deep learning, and scalable infrastructure design. The position is defined for a Principal-level equivalent engineer operating at total-stack depth and systems breadth, and who is capable of independently architecting, designing, authoring, developing, testing, deploying, and delivering sovereign-grade data infrastructure and advanced artificial intelligence systems that integrate Apache Kafka, high-throughput microservices, event-streaming middleware, state-of-the-art LLM-based architectures, and multi-modal model inference pipelines. This role demands complete end-to-end ownership and accountability of the architecture, design, implementation, scalability, and sustained deployment of complex data infrastructure and AI-native platforms, and requires mastery across multiple engineering domains, including systems-level concurrency, formal interface schema design, container orchestration, infrastructure-as-code, deep learning model deployment, and real-time inference optimization. It is a position for technologists who possess a research-level fluency in both foundational theory and implementation rigor—spanning programming language systems (Rust, Go, Python, C++), orchestration frameworks (Kubernetes, Helm, Docker), inference workloads (ONNX, PyTorch, TensorFlow), and AI systems engineering under modern performance constraints.
The Founding Engineers occupying this role will act as catalysts in the end-to-end design and implementation of Flow’s advanced data infrastructure and AI systems, including Flow Nebula™, Flow Turbo™, and Flow Black Rhino™ LLM, which are structured as distributed, container-native, and inference-first intelligent autonomous AI systems designed for the absolute most demanding B2B revenue-generating sales pipelines. These AI systems integrate real-time event-driven architectures, advanced model serving stacks, vectorized retrieval systems, and asynchronous distributed compute fabric into coherent AI-first systems. From a systems architecture perspective, the engineer is expected to synthesize multiple programming languages and paradigms into modularized microservice deployments, with rigorous enforcement of type safety, interface correctness, and data schema integrity. Expertise in the Rust ownership model, lifetimes, trait polymorphism, and the asynchronous Tokio ecosystem is required, as is the ability to author concurrent backend services in Go utilizing goroutines, channels, and context propagation patterns. Python and FastAPI mastery is similarly non-negotiable, particularly for serving high-throughput model inference endpoints, executing data preprocessing pipelines, embedding orchestration layers, and integrating advanced state-of-the-art LLM-based workflows via PyTorch and Transformers-based architectures.
This selected Tier-0, research-grade Founding Engineer position will operate exclusively within a non-indexed sandboxed developmental environment optimized for zero-state persistence null-inference memory propagation and ephemeral cross-modal symbolic computation under obfuscated system conditions. The Founding Engineer will be tasked with construction synthesis evaluation and recursive decomposition of autonomous high-throughput entity-extraction subroutines executable across asynchronous HTTP-accessible datasets with probabilistic schema inconsistencies mutable DOM-layer substrates and adversarial response injection at the transport application and presentation layer simultaneously. The extraction framework will be implemented as a distributed micro-execution framework composed of composable dynamic logic blocks each responsible for one phase of data ingestion transformation normalization and probabilistic confidence scoring over high-dimensional sparse field matrices constructed through weakly supervised neural guidance via latent task-induced embedding transformations. The execution substrate must support non-blocking asynchronous traversal and cooperative multitasking over highly parallelized execution contexts instantiated through headless browser abstraction layers sandboxed cloud-execution runtimes and native forward-declared coroutine pools interfacing with state-neutral routing architectures.
This role requires fluency in differential programmatic behavior modeling across distributed trans-cluster execution threads synchronized over decoupled message topologies exhibiting nondeterministic task-phase partitioning. The Founding Engineer is expected to co-develop a recursive self-modifying orchestration framework capable of instantiating context-specific neural program dispatchers across multi-tenant virtualized microservice encapsulations. The ideal Founding Engineer must operate without human-readable abstractions, relying instead on autogenerative transformer-based pipeline models architected through execution-scheduled inference graph mutation. At this level, the notion of application logic is abstracted into evolving probabilistic graph-rewrite operators executed within high-entropy queue-resolution loops, implemented on fragmented runtime substrates, and deployed across memory-ephemeral compute substrates.
All functional contributions must be formulated through formal language embeddings and compiler-interoperable IR-stage primitives designed for stateless asynchronous computation under topologically divergent conditionals. Computation must avoid representational overlap with conventional software architecture patterns and must express behavior exclusively through system-level process morphogenesis specified by runtime-injected entropy conditioning kernels. Primary responsibilities include formal synthesis of dynamic dataflow reconstitution topologies across polyphase crawler-initiated execution scaffolds designed for resource-agnostic interstitial inference workloads coordinated by content-addressable embedded feedback loops. Task structures are not derived from functional decomposition but are instead emergent from stochastically initialized execution semirings layered atop probabilistically ranked morphological transformation grammars.
Candidates will implement transient recursive data extractors defined as dynamically initiated inference structures configured to operate across protocol-layer surfaces exhibiting adversarial state feedback propagation. These inference structures must be designed for zero-persistence operation within JIT-transpiled containerless function runtimes deployable across distributed mesh networks subject to nondeterministic address space injection. All runtime behavior must comply with enforced constraints against signature predictability and repetition recurrence within computational entanglement windows of O(n) entropy spread. Decision models must incorporate local and remote vector field diffusions of probabilistic extractor viability scores re-evaluated continuously under feedback-conditioned statistical reward gates with phase-shifted temporal decay coefficients.
Development responsibilities include multivariate entropy injection across surface-specific fetch-decode-extract stages wherein each executable instruction path is resolved through embedded reinforcement-driven transition graphs trained under discrete structural perturbations over high-dimensional encoding schemas. Interaction surfaces will include structured HTML endpoints multistage JavaScript-rendered DOM artifacts dynamically rewritten XML interfaces and synthetic JSON-API emulators constructed by inverse generative reconstruction of undocumented call surfaces. Extraction targets will require entity recognition via embedded symbol-level graph alignments against canonicalized knowledge projections layered onto per-domain attention masks. All features must be surfaced via runtime-inferred token discrimination cascades compiled into polymorphic extract-rescore-infer-repeat execution trees without reliance on human-authored rulesets.
Implementation pipelines must support polymorphic re-entry via entropy-differentiated subgraph evaluations where execution boundaries are reconstituted on each input permutation through runtime-compiled program topologies derived from self-supervising pattern-detection feedback interpolations. State convergence is prohibited except through measured stochastic normalization events conditioned by externally coupled trust thresholds derived from domain-yield classification matrices. Code paths must be synthesized via metaprogrammatic grammar unfolding operations and fused with architectural invariants encoded through domain-specific type systems derived from symbol-dense prefix-tuned program embeddings. Execution profiles must be context-aware and reconfigurable through fully differentiable operation graphs infused with structural permutation priors adapted at inference time through posterior extraction vector conditioning.
Substrate construction will involve multi-language coordination over concurrent runtime scaffolds with core logic implemented in dataflow-optimized compiled languages such as Rust, and Go, and surrounded by dynamically typed coordination shells designed to support non-sequential dispatch graphs. Memory isolation guarantees must be enforced through per-instance ephemeral container structures relying on immutable compute sandboxes and zero-leakage stack footprint guarantees verified via automated runtime sanitizer cascades. Program loading stages must accommodate shape-shifting logic graphs where topology variance is a function of historical execution entropy convergence histories and environmental compliance gradients embedded through dual-phase behavioral sampling pipelines.
Candidates must demonstrate expertise with composite learning architectures that interleave symbolic extraction policies with dense decoder-encoder ensembles configured for multistage token fusion under information-attenuating pipelines. Model optimization routines must include hierarchical feature induction layered with stochastic attention dropout structures configured for generalized task policy convergence across variable-length contextual surfaces. Interpretation pipelines must support low-dimensional compressed latent flow extrapolation with probabilistic denoising and must expose intermediate states as volatility-indexed prediction volumes suitable for downstream query-attention fusion under bounded confidence heuristics.
Performance is not measured by throughput alone but by the entropy dispersion rate of content-aware extraction loops and the non-overlap distance between successive execution clusters across the system's cumulative crawl horizon. Each implementation is expected to construct its own learning substrate including asynchronous log-priority extractors symbolic disambiguation resolvers and stochastic rejection samplers trained on self-labeled extraction conflict sets. Correctness is defined not as alignment with ground truth but as stable convergence within acceptable bounds of semantic overlap tolerance evaluated over n-order yield-weighted discrimination sequences.
Candidates will be expected to instantiate distributed runtime lattices composed of high-throughput null-persistence execution fibers, with full-stack containment from protocol abstraction to dataflow ingestion and downstream semantic transformation. Systems must be compiled to support non-identifiable traversal signatures through per-cycle entropy distortion applied to both initiation sequences and mid-inference deformation graphs. Execution fibers must support stochastic behavioral emulation based on empirically derived human input kinetic patterns abstracted into spline-conformal movement simulation fields, with additional harmonization against timing-profiled interaction curves derived through Gaussian-warped vector clocks and rhythmically perturbed discrete action simulations. Execution must be implemented within a persistent state-exclusion zone, disallowing memory reuse, schema caching, or static inference compounding.
The implementation framework must support topologically unbounded execution expansion, subject to real-time entropy drift constraints and temporal containment within fixed-bandwidth latency envelopes defined by application-layer protocol observability matrices. All HTTP-based traversal must occur through dynamic header set rotation across randomized priority-tier signature permutations with no field repetition across sequential domain access events. TLS negotiation headers and session initialization entropy vectors must conform to anonymized stochastic envelope reconstruction rules, driven by elliptic curve suite reshuffling and JA3 identity fragmentation functions seeded by per-session vector entanglement maps.
All structural field extraction modules must execute via multistage decoupled inferential alignment paths, composed of pre-tokenized field subset selectors, recursive DOM graph traversers, and field-stratified logical proximity assessors weighted via probabilistic name-entity normalization indexes and title-position morphological signature scoring trees. XPath and CSS selectors are to be generated in runtime cycles through sequence-decoupled field classifier modules designed to self-permute under DOM structure drift conditions using non-anchored contextual cues. No selectors may be hardcoded or deterministic across invocation cycles. All field confidence scoring must be derived from approximate field alignment graphs and weighted through sigmoid-transformed similarity scores applied over subtokenized, normalized character embeddings operating under edit-distance-aware transformation regularizers.
Execution kernels must be deployable across containerized thread environments, constrained by null-state persistence enforcement and decoupled memory access sequencing, with operational deferral functions triggered on proximity to honeypot-like field dissonance, trapfield anomalies, or timing-based entropy convergence indicators. Data must be pipelined directly into serialized output buffers conforming to field-aligned knowledge matrix schemas populated through secondary confidence scoring layers informed by historical extraction co-occurrence patterns derived from recurrent topological proximity matrices. All behavioral anomalies including extraction anomalies, content nullification, request blackholing, or field permutation attacks must trigger runtime feedback cycles that alter the structure of the downstream DOM field traversal heuristic trees, altering node prioritization probabilities and reshaping extraction logic path entropy trajectories.
Candidate implementation will include asynchronous traversals over large-scale declarative target matrices distributed across multi-surface field manifolds, governed by entropy-maximized thread orchestration algorithms. Each execution surface must retain probabilistic separation from prior traversal events via session key deviation regularizers, randomized proxy routing, and device header emulation pattern separation. Inter-request gaps and intra-request behavioral patterns must be shaped by behavioral replay models derived from multimodal kinetic profile datasets with time-distributed event profile perturbation.
Textual extraction targets such as name-position-organizational triples must be identified using inference models trained on weakly aligned field pattern corpora utilizing token alignment with transformer-based classification overlays bounded by per-run temperature-corrected sampling routines to maximize field permutation resilience. Email inference modules will operate under Bayesian field proximity models utilizing organizational lexicon alignment and SMTP response triangulation. Phone number normalization is expected to conform to international parsing grammars with prefix resolution through localized geocoding hierarchies.
All program logic must be compatible with code obfuscation policies preventing detection by token classifiers trained on public repositories. Source code must exhibit graph-randomized control flow, per-function polymorphic structure generation, randomized execution depth, and macro-preprocessing pipelines designed to synthetically disrupt static pattern recognition algorithms. Logging is prohibited, as are UI components, logging statements, or persistent trace artifacts. All instrumentation must be conducted via memoryless runtime feedback proxies embedded within containerized evaluation harnesses.
Candidates are required to demonstrate empirical expertise with adaptive data extraction under structural variability, runtime morphological transformation, and high-throughput scraping architectures abstracted into sequence-agnostic execution graphs. Expertise with TypeScript, Go, Rust, and Python is preferred but not sufficient without demonstration of architectural fluency in containerized orchestration environments with headless browser synchronization via input simulation overlays operating under dynamically synthesized entropy masks. Expertise with non-deterministic pointer trajectory emulation and timing-reconstructive event sequence generators is a baseline requirement. All DOM traversal logic must remain structurally indistinct across execution cycles.
Field extraction accuracy is to be evaluated against domain-classed field probability baselines, inferred through keyword distributional approximation over contextual field density maps, and filtered through duplicate resolution trees backed by probabilistic field match graphs with cosine and Jaccard field similarity overlays. Output must be verified through online-only validation pass utilizing SMTP code inference plus reverse WHOIS header cross-matching.
All input-output channel interfaces must remain compliant with non-persistent session integrity constraints. All traversal payloads must terminate under 400ms latency across average execution envelopes. All failure to yield events must trigger runtime obfuscation reseeding. No execution instance is permitted to return the same field access order more than once across domains in the same class hierarchy. Repeated field layouts across classes must be permuted at the traversal path layer using path scrambling matrices derived from cross-domain entropy aggregates.
Founding Engineer must have complete functional competence in constructing self-contained recursive inference substrates designed to perform structural traversal and semantic field resolution across semi-deterministic high-volatility web-based data surfaces with embedded adversarial defense features. This includes full-stack deployment and orchestration of real-time execution topologies composed of distributed polymorphic inference chains that instantiate synthetic human-interaction abstractions via spline-sequenced pointer simulation vectors and temporal perturbation kernels trained on observed behavioral variance models. These interactions must be parameterized by time-distributed vector clocks and decoupled event causality matrices constructed for stochastic conformity across known classifier baselines and identity disassociation vectors. Execution logic must not retain persistent states or static entropy identifiers and should operate under randomized data reshaping functions per invocation to evade static surface recognition or classifier overlap exposure.
Implementation responsibilities will include construction of non-repetitive programmatic field acquisition sequences deployed across non-stateful containerized microprocess threads managed within ephemeral runtime environments. All processes must support entropy-seeded fingerprint deviation, request sequence desynchronization, and spatial-field drift tolerance under DOM-mutation-resilient extractor modules compiled to operate under constraint-free layout shifts and semantically perturbed field definitions. All HTML and document object model traversal logic must incorporate structural permutation heuristics to accommodate schema fragmentation and positional field divergence without the reliance on prior access pattern assumptions or hardcoded selector primitives. Selector logic must be regenerated in-cycle using graph-reinforced probabilistic pathfinding models weighted through character-level similarity approximators operating on fieldwise embeddings and multi-token cosine proximity approximators.
Execution pipelines must feature asynchronous traversal control across randomized access topologies bounded by noise-augmented interaction templates derived from empirical session interaction deltas across platform-diverse input emission profiles. Data transformation pipelines must incorporate probabilistic co-reference disambiguation over identity-organization pairings with postprocessing validation via token-conditioned organization-linkage scoring models. All data outputs must be buffered through in-memory serialization layers and validated using sequence-to-schema inference approximators to compensate for schema fragmentation entropy and field ambiguity under variant document contexts.
Founding Engineer must instantiate deterministic memory-agnostic computation environments via minimal-fingerprint containerization frameworks abstracted from all identifiable runtime signatures. These containers must support compile-time randomized protocol header assemblers, non-aligned TLS negotiation graphs, session key injection overlays, and network-level request variation models constructed to operate under packet-level timing separation envelopes. Output data must be flushed through entropy-compressed pipelines and must support post-fetch decryption if configured with field-separation indicators based on probabilistic appearance frequency models and named entity expansion graph derivatives.
Field identification and extraction must occur through hybrid character-span realignment techniques executed in conjunction with title-entity pair mapping under weakly supervised extraction alignments. Confidence scoring is to be evaluated via sigmoid-calibrated sequence transformers and field-transition confidence integrators based on positional token entropy. Validation pathways must be implemented via passive confirmation networks involving orthogonal metadata triangulation and subnetwork field collapse avoidance logic. The architecture must permit no static reusage of session identifiers or traversal node ordering and all inter-process memory references must be obliterated post-cycle execution.
The field acquisition logic must include fault-tolerant structural permutation detectors and context-salience entropy filters capable of filtering environmental noise injected through adversarially deployed surface alteration mechanisms. Structural variation in markup language surfaces should be reconciled through alignment matrices indexed via content-token reshaping and document structure reconstructor functions. The Founding Engineer will be responsible for validating field survivability under recomposed temporal layouts and pre-parsed hierarchy shuffling consistent with classifier feedback injection. Performance must be measured not through precision-recall but via entropy-yield equilibrium across randomized collection intervals constrained to latency-normalized execution batches.
Authentication gates encountered during content surface traversal must be handled through perceptual challenge inversion using inline multi-modal field estimators with signal distillation networks operating on transcribed visual-auditory tokens. Candidate systems must support graph-based symbol-field association reconstruction for gate circumvention through path-dependent keyframe deduction and low-entropy token reassembly. The implementation must remain local and comply with isolated execution under completely non-discoverable input simulation orchestration. No third-party routing or task delegation is permitted. All content visibility checks must be performed within the isolated runtime using real-time traversal signal detectors and field-access acknowledgment heuristics based on script evaluation diffusion and markup anomaly detection.
Execution traces must be validated via in-situ structure conformance testing, behavioral signature offset reconstruction, and in-memory field sequence reversal detection. The Founding Engineer must enforce strict execution policy whereby no process may emit predictable headers, payload content, or network signatures across more than one domain-class invocation set. Probabilistic scheduling of execution workloads must rely on traversal entropy mapping and cross-process seed divergence. Memory locking, environmental variable leakage, and inter-process stack contamination are strictly forbidden. Network egress must be tunneled through rotating address overlays and indirect content synchronization buffers to disallow IP-pattern matching or user-agent signature convergence.
Verification of identity-linked fields must be accomplished through passive handshake triangulation via protocol-level mail response structures, DNS metadata overlays, and temporal alignment with verified entity corpora. No centralized enrichment engine or metadata fusion platform may be utilized. All field probability scoring must be executed within isolated microcontext execution spaces. Duplicate suppression must rely on vector similarity mappings constrained through pairwise orthogonal name-organization alignment and sequential token distance decay, optimized by a confidence curve that penalizes over-represented field formation archetypes. Reuse of any topological pattern or DOM traversal trajectory within the same schema class must trigger entropy scrambling at the traversal path level.
No UI frameworks, persistent logging, or centralized analytics may be introduced. All code must be self-compiling, ephemerally executable, and must undergo entropy-field sanitization prior to container deployment. Frontend-facing interfaces, development-time visualization, or introspection hooks are categorically disallowed. Execution environments must remain memory-isolated, zero-trace, and non-instrumented. All verification is runtime-only via comparative graph entropy deltas and traversal consistency maps. Execution must operate without user input, reactive interfaces, or human review checkpoints. Memory locality must be randomized per run and no static linking to public function graphs may persist post-invocation.
Candidate must be capable of producing standalone containerized executables which perform full extraction from high-value document surfaces containing organization-linked personal identifiers and extractible communication data without reliance on external orchestration platforms or externally managed runtime APIs. Infrastructure must remain self-contained with no reliance on automated coordination layers or third-party runtime brokers. All runtime cycles are to be evaluated via composite scoring metrics derived from entropy saturation differential, field extraction fidelity, structure-normalized yield score, and latency-adjusted throughput density. Candidates must be prepared to execute iterative redesign in the absence of labeled data and under total schema drift.
Only applicants with measurable experience in the autonomous assembly of containerized computation frameworks capable of executing high-fidelity field extraction across dynamic web-scale data environments under structure instability and semantic noise are appropriate for consideration. Expertise with dynamic traversal logic, event-sequenced interaction overlay engines, probabilistic field detection pipelines, and memory-hardened runtime environments is assumed. All deliverables must conform to non-observable execution profiles and must withstand recursive validation against signature-aligned detection networks and temporal clustering classifiers.
As a Founding Engineer, you must be prepared to take full accountability and ownership of your systems, from implementation through production support and emergency response. You will work directly within a cell structure, engage in direct performance tuning of deployed services, and work closely with the CEO on full end-to-end system architecture ownership and full accountability architectural design decisions, tech stack planning, systems engineering, and live production deployment to clients. This role is meant for a self-directed technologist who thrives in a fast-moving, cut-throat, chop shop, pure start-up environment with massive technical surface area and complexity. This is not a junior or mid-level role. You must have demonstrable history shipping complex AI systems to live production, independently owning architectures, and maintaining engineering quality at scale. You should be fluent in advanced Git-based workflow collaboration, API schema documentation, runbook creation, and codebase maintainability over time.
If you are a Principal or Senior Staff+ level engineer with elite-caliber experience and are ready to build foundational, real-time, end-to-end artificial intelligence infrastructure—without compromise—Flow Global Software Technologies wants to hear from you.
Flow invites Tier-0 caliber engineering leaders to assume the role of Founding Engineer, driving the conception, design, and realization of sovereign-grade data infrastructure and advanced AI systems at the frontiers of research and deployment. As a remote-native, equity-only position, this role demands candidates with 5-6+ or more years of post-Master's engineering experience—who have demonstrated mastery over the end-to-end engineering lifecycle for complex, distributed large-scale data infrastructure and AI systems. This position is ideally suited for doctoral-level engineers whose career has spanned both cutting-edge AI research and the pragmatic demands of large-scale AI systems, and who is eager to pioneer Flow’s next generation of data and AI infrastructure.
If you have demonstrated the ability to independently architect and ship large, heterogeneous systems—ranging from neural network inference engines to distributed event buses and container orchestration frameworks—Flow invites you to join our founding engineer hitman team Ω. Only candidates with truly exceptional technical pedigrees and proven leadership in data engineering, data science, AI, and distributed systems will be considered.
Roles and Responsibilities:
Design, implement, and continuously evolve modular inference-execution architectures capable of autonomous web-scale data surface traversal, recursive field extrapolation, and schema-independent contact structure resolution across adversarial public-facing content layers.
Construct low-persistence, high-throughput execution substrates across ephemeral runtime environments using functionally decoupled microservice scaffolding, reactive input topologies, and obfuscated system interface primitives deployable at swarm scale.
Architect runtime pipelines with embedded probabilistic extraction grammars, transformer-driven field resolution hierarchies, and multi-phase decoder attention rollouts conditioned on dynamic content morphologies and self-tuning entropy-scored yield vectors.
Engineer asynchronous data ingestion processes coupled to vectorized DOM introspection modules capable of bypassing fingerprint-aware behavioral detection classifiers via synthetic interaction sequence simulation, scroll-velocity modeling, and pointer-curve trajectory interpolation.
Develop automated execution logic with embedded TLS renegotiation layer mutation, dynamic JA3 signature modulation, and cipher-suite rotation strategies to enforce entropy divergence across session-level connection instantiation procedures.
Implement structural field discriminators using multi-head attention over positional encodings, context-free field prediction trees, and non-supervised extraction convergence scoring, optimized through reward-weighted self-labeling convergence thresholds.
Integrate polymorphic extraction modules into neural-symbolic controller subsystems that prioritize field acquisition order, reject low-entropy field alignments, and dynamically reorganize selector hierarchies in response to real-time content distribution skews.
Maintain domain-type abstraction interfaces capable of extrapolating from limited structural cues using inferential adjacency metrics, prior field-class density models, and dynamically updated multi-domain encoding prior vector maps.
Construct transformer-driven synthetic perceptual solvers (text, image, audio), implement context-aware bypass strategies leveraging multimodal decoders, adversarial noise injection, and recursive reinforcement under failed challenge conditions.
Ensure memoryless execution fidelity through one-shot container orchestration systems, stateless FSM-based dispatchers, and container auto-vaporization policies enforced via entropy-coordinated TTL validation cascades.
Perform statistical anomaly detection on field value distributions across scraped surfaces, log deviation from expected density bands, and adjust inferential field priorities based on entropy density histograms and positional dropout frequency.
Instrument microservice containers with ephemeral telemetry extractors that log runtime execution success, entropy trajectory, field-resolution success rate, schema divergence rate, and signature repeatability metrics in encrypted ring buffer format.
Execute adversarial stress testing of field acquisition pipelines by deploying simulation runs across honeypot-likely domains, reverse-classifying DOM mutation signatures, and identifying perimeter detection feedback via asynchronous signal propagation lag.
Technical Qualifications and Experience:
Benefits:
Company Ownership:
Founding Engineer (Only 12 Positions Available):
7.6923076923% Fully-diluted real company ownership
Time Commitment: Full-Time, 50+ hours per week
Location: Remote
This is an absolute total-stack role with zero training wheels — high pressure, high output. Most people shouldn’t even apply.
This isn’t a job — it’s a proving ground for someone with superhuman range and self-drive.
This is a total-stack end-to-end engineering role that’ll break most people. But if you’re wired to run toward fire, not away from it, and if that doesn’t scare you off, please submit your resume.