Applied AI Engineer

San Francisco, CA

• On-site (5 days/week)
• Full-time Compensation: $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