Applied AI Engineer

In this role, you will own the intelligence layer behind AI automation for enterprise finance operations, moving ideas from research into production. Your focus includes browser-agent reliability, document understanding, and inference optimization, with continuous improvements driven by measurable system performance.

Key Responsibilities

• Own the core intelligence that powers the client’s automation workflows.
• Translate research work into production systems, with emphasis on browser-agent reliability, document understanding, and inference optimization.
• Improve accuracy and speed on an ongoing cadence, making the system better every week.

Required Qualifications

• Strong Python and applied ML engineering experience using ML frameworks, especially PyTorch.
• Applied ML/AI engineering background from a strong company, with evidence of real delivery.
• An eval-and-metric mindset, focusing on production metrics rather than only benchmark results.
• Comfort working with messy data and figuring out how to make it usable in production.
• A track record of shipping end-to-end systems, with the ability to describe specific builds beyond research prototypes.
• Clear, concise communication about your work without relying on buzzwords.
• Based in San Francisco or willing to relocate; able to work in-person 5 days a week.

Technologies and Tools

• Python, PyTorch, and modern ML frameworks
• LLMs, agents, RAG, and fine-tuning
• Quantization, caching, and routing for inference optimization

Nice to Have

• Real applied ML/AI work at a respected Series A to D startup or a selective technical organization (examples listed: Ramp, Databricks, Scale, Stripe).
• Lab or research exposure (examples listed: SAIL, BAIR, MIT CSAIL) paired with evidence of shipping, not only publishing.
• Recent progress toward LLMs, agents, RAG, fine-tuning, or production-focused ML systems.
• Experience with RL, retrieval systems, or agent-based systems.
• Breadth across production responsibilities such as inference optimization, data pipelines, fine-tuning, and model monitoring.
• Published ML papers and/or significant OSS contributions.

Why This Role

• Work on a category-defining problem: building AI that can operate software end to end to address a $300B+ market.
• Top-tier backing from an infrastructure-focused investor.
• Enterprise momentum with live customers ranging from $500M to $5B in revenue.
• Focus on frontier research-to-production areas, including browser-agent reliability, document understanding, fine-tuning pipelines, and inference optimization, with shipping improvements every week.
• Ground-floor ownership within a six-person team in San Francisco, where you will own the intelligence layer powering the product.

Job Details

• Location: San Francisco, CA (on-site)
• Work policy: On-site, 5 days/week
• Compensation: USD 180,000 to 250,000 per year + competitive equity
• Visa sponsorship: Available (H-1B, O-1)
• Employment type: Full-time