Applied ML Engineer for Enterprise AI Agents

Varick Agents LTD.; San Francisco (CA); On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Equity

Flexible PTO

Free lunch & dinner in office

Ride home after late work

Monthly team dinners

Job summary

Varick Agents LTD. is seeking a production-focused ML engineer to own post-training loops for enterprise-grade AI agents in our San Francisco environment. You will convert deployment data into models, evaluation data, and iterative improvements to boost agent accuracy beyond demo-grade levels.

The role emphasizes applied ML, real-world usage, and cost-aware inference, with a fast-paced small-team culture and opportunities to influence how agents operate inside major enterprises.

Qualifications

• Strong applied ML background with hands-on post-training experience (SFT, RLHF/DPO, or similar) on LLMs.
• At least one model or system you took to real users, and a clear account of how it behaved in production.
• Fluency with the modern training stack and a working grasp of inference economics.
• Evaluation discipline: you trust measured results over vibes.
• Comfort moving fast in a small team, with judgment on where rigor matters.

Responsibilities

• Own fine-tuning, preference optimization, and post-training pipelines that lift agent performance on real enterprise workflows.
• Build the eval suites and golden sets that measure whether a change actually helped, before it ships to a client.
• Turn traces and failure cases from production into training data and targeted improvements.
• Work with the platform team on the model layer: routing, distillation, cost and latency budgets.
• Keep a clear-eyed view of where fine-tuning earns its keep versus better prompting, retrieval, or scaffolding.

Skills

Applied ML

Post-training experience on LLMs

Evaluation disciplines

Job description

Varick Agents LTD. is seeking a production-focused ML engineer to own post-training loops for enterprise-grade AI agents in our San Francisco environment. You will convert deployment data into models, evaluation data, and iterative improvements to boost agent accuracy beyond demo-grade levels.

The role emphasizes applied ML, real-world usage, and cost-aware inference, with a fast-paced small-team culture and opportunities to influence how agents operate inside major enterprises.