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About the Role
You will own the post-training loop for AI agents, using real workflow data to improve model accuracy beyond demo-grade performance. This is applied, production-facing research with results shipping to enterprise clients.
Responsibilities
- Own fine-tuning, preference optimization, and post-training pipelines to enhance agent performance on enterprise workflows.
- Build eval suites and golden sets to measure the impact of changes before client deployment.
- Transform production traces and failure cases into training data and targeted improvements.
- Collaborate with the platform team on the model layer, including routing, distillation, and cost/latency budgets.
- Differentiate the value of fine-tuning against alternative methods like prompting, retrieval, or scaffolding.
Requirements
- Strong applied ML background with hands-on post-training experience (SFT, RLHF, DPO, or similar) on LLMs.
- Experience taking at least one model or system to real users, with a clear account of its production behavior.
- Fluency with the modern training stack and a working understanding of inference economics.
- Demonstrated evaluation discipline, prioritizing measured results over subjective assessments.
Skills
- Fine-tuning
- Preference optimization
- Post-training pipelines
- Evaluation suites
- Model layer development
- Prompting
- Retrieval
- Scaffolding
- SFT
- RLHF
- DPO
- LLMs
- Modern training stack
- Inference economics
Location
- San Francisco, CA
- On-site
Work Type
- Full-time
- On-site
Experience Level
- Applied ML background
- Hands-on post-training experience
Benefits
- Meaningful equity
About the Company
- Varick builds AI agents that automate operational workflows within large enterprises.
- The company utilizes a forward-deployed engineering and strategy model to map workflows, build Proofs of Concept, and bring agents into production.
- Varick is venture-backed, revenue-generating from day one, and has agents in production at multiple large companies.
- The team comprises individuals from top organizations like Meta, AWS Bedrock, Citadel Securities, McKinsey, BCG, and Stanford.
Equal Opportunity
- All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.
- Employment is subject to a standard confidentiality and non-disclosure agreement.