Applied AI Engineer

David Joseph & Company; San Francisco (CA); On-site; USD 180,000 - 250,000; Full time

Job summary

David Joseph & Company in San Francisco, CA is seeking an experienced ML engineer to drive applied AI automation for enterprise finance operations. You will own the intelligence powering the client’s automation and translate research into production for browser-agent reliability, document understanding, and inference optimization.

The role requires strong Python and ML engineering experience with PyTorch, a production-focused mindset, and the ability to ship end-to-end systems on-site in San

Qualifications

• Strong Python and ML engineering experience with PyTorch as core framework.
• Applied ML/AI engineering experience at a strong organization.
• Eval-and-metric mindset focusing on production metrics, not just benchmarks.
• Comfort working with messy data and turning it into usable inputs.
• Demonstrated ability to ship end-to-end systems, not only research.
• Crisp communication about your work without buzzwords.

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.

Skills

Python

ML engineering

PyTorch

Eval-and-metric mindset

Messy data handling

Ship end-to-end systems

Crisp communication

Tools

PyTorch

LLMs

Job description

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