Halluminate — Member of Technical Staff, Platform Engineering

Halluminate — Member of Technical Staff, Platform Engineering Type: Full-time | On-site | San Francisco, CA (5 days in-office) Compensation: $200K–$250K + 0.15–0.3% equity Hiring count: 6–8 Visa sponsorship: Open to visa transfers (e.g. OPT, H-1B transfers) Reports to: Wyatt Marshall, Co-founder / CTO About Halluminate Halluminate provides fully managed environments and benchmarks so teams can train computer agents with superhuman capabilities. The company works with frontier labs to build the "Financial Services Super Intelligence" — high-quality computer and tool-use RL gyms that train AI agents to do work across financial services verticals (Excel modeling, PowerPoint creation, and more across Investment Banking, Private Equity, and Hedge Funds). OpenAI, Anthropic, and the largest frontier-model companies are paying customers. Founded: 2024 | Team size: 6 | Total funding: $8.5M (recently raised $6M at an $80M valuation, YC S25) Industry: AI, Data, Financial Services, Finance, Enterprise Website: halluminate.ai Office: Dogpatch, San Francisco Why Candidates Should Join Frontier customers: OpenAI, Anthropic, and the biggest frontier-model companies are paying customers — direct exposure to researchers at those labs. Traction: Went from hundreds of thousands in revenue in 2025 to nearly $10M closed this year, targeting mid-eight figures by year-end. Founding ownership: Own platform engineering and build the engineering org and culture from the ground up. Unusually strong seed-stage benefits: Fully covered healthcare, $10K+ relocation, 401k with 4% match, free meals, gym, and transportation (Ubers/Waymo). Intake Call Summary Company: Builds RL environments for financial services, partnering with large AI labs (OpenAI, Anthropic). Founded by CS graduates; focus on creating realistic datasets for AI-agent testing. Role: Platform engineer with full-stack capabilities, ideally backend-leaning. Python, TypeScript, and infra knowledge expected. Docker and AI coding tools essential; cloud (AWS, S3) a plus. Candidate profile: ~5 years experience is ideal, but open to 3+ years with strong side projects or internships. Preference for startup experience over large traditional companies. Well-rounded people comfortable with both customer interaction and technical work; former founders / early-stage startup employees desirable. Work arrangement: In-person preferred with flexible hours; some employees work remotely in evenings. Not a strict 996 mandate, but people often put in extra hours and weekends voluntarily. Compensation: $150K–$250K with 0.25–0.5% equity per intake (conflicts with posted fields — see flag above). Flexible based on experience. Interview process: Intro call team interviews work trial (remote or onsite, typically 1–3 days). Pain points / urgency: Scaling to meet new contracts and demand from AI labs; building out internal platforms and infra. Filling roles quickly, immediate start preferred, open to recent grad-school graduates. The Role Founding Member of Technical Staff owning platform engineering, developing frontier long-horizon RL environments, and helping build the engineering org from the ground up. What You'll Be Doing Build RL environment training/inference infrastructure to support customer usage at scale Research and develop next-generation RL environments that are increasingly realistic, long-horizon, and difficult for frontier models Build software to 10–100x the quality and throughput of RL environment creation Innovate on synthetic data pipelines to create realistic problems Create platform analytics tracking RL environment costs, hours, bottlenecks, and subject-matter-expert management Craft domain-specific verifiers for financial services verticals and beyond (verifiable rewards for PowerPoint building, Excel modeling, quantitative trading) Establish engineering culture and practices from the ground up Tech stack: Python, TypeScript, Docker, AI coding tools; cloud (AWS, S3) a plus Qualifications Seniority 2–8 years of experience in platform/full-stack engineering, building RL environments or ML infrastructure [Required] Work Experience Experience at a Pre-seed/Seed/Series A startup, or a former founder [Must have] Experience as a platform, full-stack, or backend software engineer in fast-moving, high-growth companies [Required] Big tech experience before going to a startup [Strongly preferred] Built RL environments, evals, or benchmarks for AI agents (Scale AI, Turing, Handshake AI, etc.) [Strongly preferred] Experience or significant interest in fintech or financial services [Strongly preferred] Education CS, ML, or related technical degree [Required] Hard Skills Experience building scalable platforms for internal or external customers (ML tooling, benchmarking, or agent products) [Required] Domain-specific verifier or reward function design [Strongly preferred] Miscellaneous Heavy user of AI coding tools and shipping at high velocity [Must have] Currently lives in the Bay Area and willing to work in person 5 days a week [Required] Traits to Avoid No startup experience / 5+ years in big tech Pure researcher or MLE Role Details Salary: $200K–$250K (intake call cited $150K–$250K — conflict) Equity: 0.15–0.3% (intake call and "Why Join" cited 0.25–0.5% — conflict) On-site policy: 5 days in-office, San Francisco Visa sponsorship: Open to visa transfers (e.g. OPT, H-1B transfers) Employment type: Full-time Location: San Francisco, CA (Dogpatch) Hiring count: 6–8 Screening Questions What's the most autonomous project you've owned end-to-end? (optional) Interview Process Per the intake call (the on-page "3 steps" detail was not itemized in the pasted HTML): Stage 1 — Intro Call Initial screen with the team. Stage 2 — Team Interviews Interviews with the founding/engineering team. Stage 3 — Work Trial Remote or onsite, typically 1–3 days. Offer Extended Candidate Hired Confirm the exact 3-step structure against Paraform — this is reconstructed from the intake summary, not the itemized interview-process field. Ideal Companies & Backgrounds No dedicated Ideal Companies section was present in the pasted HTML. The only companies named inline (in the RL-environments requirement) are: RL environments / evals / benchmarks Scale AI, Turing, Handshake AI Ideal Candidate Profiles For reference only — do not source these specific profiles. Red Giuliano — LinkedIn CTO at Stealth Startup | New York, United States Wyatt called this "probably the closest to a perfect resume" Best combination of enterprise technical work plus startup founder skillset Data science and ML at large-scale production enterprise capacity Strong finance overlap Founded his own startup (Zero-True) — ships fast, decides with limited info, iterates quickly, acts autonomously Rohan Bansal — LinkedIn Software Engineer | United States Google Cloud Storage team — infra and production experience Finance domain experience via quant research at an arbitrage firm Generally strong education and internship experience Area for improvement: slightly less startup/founder experience than Red Rejected Candidate Feedback Prioritize candidates with early-stage startup experience — ensure they've built products from 0-to-1 rather than coming from big tech or research-only backgrounds. Focus on profiles that demonstrate ML tooling / agent platform ownership — hands-on experience creating and scaling RL environments and evaluation systems. Only consider candidates who are Bay Area-based or willing to relocate immediately to meet the strict 5-day in-office requirement.