San Francisco, CA · On-site (5 days/week) · Full-timeCompensation: $180,000–$250,000 + competitive equity
About the Company
Our client builds AI that operates computers the way humans do — navigating browsers, processing documents, and working through legacy systems — to automate the messiest enterprise finance operations. It's going after the $300B+ BPO industry built on labor arbitrage that software historically couldn't touch, because people were the product. It recently raised a seed round from a top-tier infrastructure investor. Early customers range from $500M to $5B in revenue, including a large property-management company automating accounts payable and invoice processing, and a major European retailer reconciling orders.
Industry: AI Tools / enterprise finance automation
The Role
Own the intelligence that powers the client's automation. You'll turn research into production across browser-agent reliability, document understanding, and inference optimization — making the system more accurate and faster every week.
Tech stack: Python, PyTorch, and modern ML frameworks; LLMs, agents, RAG, and fine-tuning; inference optimization (quantization, caching, routing).
Requirements
- Strong Python and ML frameworks, particularly PyTorch
- Applied ML/AI engineering experience at a strong company
- An eval-and-metric mindset — thinks in terms of production metrics, not just benchmarks
- Comfort with messy data and figuring out how to make it useful
- A track record of shipping — can describe specific systems built end to end, not just research
- Crisp communication about their own work, without buzzwords
- Based in San Francisco or willing to relocate; in-person 5 days a week
Nice to Haves
- Real applied ML or AI-engineering work at a respected Series A–D startup or selective technical org (e.g., Ramp, Databricks, Scale, Stripe)
- Lab or research exposure (e.g., SAIL, BAIR, MIT CSAIL) paired with evidence of shipping, not just publishing
- Recent momentum toward LLMs, agents, RAG, fine-tuning, or production ML systems
- Experience with RL, retrieval systems, or agent-based systems
- Cross-stack range: inference optimization, data pipelines, fine-tuning, and model monitoring
- Published ML papers or significant OSS contributions
Why Join
- A category-defining problem: building AI that actually operates software end to end to attack a $300B+ market
- Top-tier backing from a standout infrastructure investor
- Real enterprise traction: live customers from $500M to $5B in revenue
- Frontier research-to-production work: browser-agent reliability, document understanding, fine-tuning pipelines, and inference optimization — shipping improvements every week
- Ground-floor ownership: a six-person team in SF; this hire owns the intelligence layer that powers the whole product
Details
- Location — San Francisco, CA
- Work policy — On-site, 5 days/week
- Compensation — $180,000–$250,000 + competitive equity
- Visa sponsorship — Available (H-1B, O-1)
- Employment type — Full-time