Applied AI Engineer

Drive applied AI automation for enterprise finance operations by taking research concepts to reliable, production-grade systems.

Responsibilities

• Own the intelligence powering the client’s automation
• Translate research into production for browser-agent reliability, document understanding, and inference optimization
• Continuously improve system accuracy and speed on a weekly cadence

Requirements

• Strong Python and ML engineering experience, with PyTorch as a core framework
• Applied ML/AI engineering experience at a strong organization
• Eval-and-metric mindset: focuses on production metrics rather than benchmark-only results
• Comfort working with messy data and improving it into usable inputs
• Demonstrated ability to ship end-to-end systems (not only research)
• Crisp communication about your own work without buzzwords
• Based in San Francisco or willing to relocate; in-person 5 days a week

Technologies

• Python, PyTorch
• LLMs, agents, RAG
• Fine-tuning
• Inference optimization: quantization, caching, routing

Additional Stack

• Modern ML frameworks built around Python and PyTorch
• LLM-based workflows including agents, RAG, and fine-tuning pipelines
• Production inference optimization using quantization, caching, and routing

Nice to Haves

• Applied ML/AI engineering work at a respected Series A–D startup or selective technical org (examples: Ramp, Databricks, Scale, Stripe)
• Lab or research exposure (examples: SAIL, BAIR, MIT CSAIL) paired with evidence of shipping, not only publishing
• Recent momentum toward LLMs, agents, RAG, fine-tuning, or production ML systems
• Experience with RL, retrieval systems, or agent-based systems
• Experience across inference optimization, data pipelines, fine-tuning, and model monitoring
• Published ML papers or significant OSS contributions

Job Details

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