Applied AI Engineer

San Francisco, CA · On-site (5 days/week) · Full-time Compensation: $180,000–$250,000 + competitive equity About the Company An early-stage, venture-backed AI startup building systems that operate computers the way humans do — navigating browsers, processing documents, and working through legacy systems — to automate the messiest enterprise finance operations. The company is going after the $300B+ BPO industry that software historically couldn't touch, and is already live with enterprise customers ranging from $500M to $5B in revenue. Founded 2025 · ~6 people · Industry: Applied AI / enterprise automation The Role Own the intelligence that powers the 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. What you'll be doing Push core automation capabilities to state-of-the-art: UI interaction, unstructured-data parsing, and tool use. Build adaptive systems that self-heal when environments change. Design fine-tuning pipelines that learn from customer-specific workflows. Optimize latency across the stack via model selection, quantization, caching, and routing strategies. Improve browser agent reliability and document-understanding accuracy on real enterprise data. 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. Eval-and-metric mindset — thinks in terms of metrics that matter in production, not just benchmarks. Comfort with messy data and figuring out how to make it useful. Track record of shipping — can describe specific systems built end-to-end, not just research. Crisp communication about own work — can describe what they built in a few clear sentences without buzzwords. Based in San Francisco or willing to relocate; in-person 5 days a week. Green Flags Real applied ML or AI engineering work at a respected Series A–D startup or selective technical org (calibration anchors: Ramp, Databricks, Scale, Stripe). Lab or research exposure (SAIL, BAIR, MIT CSAIL, or similar) paired with evidence of shipping, not just publishing — the combination is the highest-signal background. Recent momentum toward LLMs, agents, RAG, fine-tuning, or production ML systems; direct adjacency to the roadmap (browser agent reliability, document understanding, inference optimization). 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. Red Flags Resumes or LinkedIn profiles stuffed with 300–400 word descriptions full of buzzwords and keywords. Inability to clearly articulate what they actually built and how they thought through problems. Communication style that sounds like reading off a script or cue card. Why Join Category-defining problem: AI that actually operates software end-to-end against a $300B+ market. Frontier research-to-production work on browser agents, document understanding, and inference optimization. Ground-floor ownership on a small SF team, owning the intelligence layer of the product. Live enterprise customers and strong early traction. Details Location: San Francisco, CA Work policy: In-person, 5 days a week (relocation supported) Compensation: $180,000–$250,000 + equity Visa sponsorship: H-1B, O-1 Employment type: Full-time #J-18808-Ljbffr David Joseph & Company