AI Researcher

Posted Yesterday
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San Francisco, CA, USA
In-Office
200K-250K Annually
Entry level
Artificial Intelligence • Software • Automation
The Role
Conduct applied, production-facing AI research to improve enterprise agents. Own fine-tuning, preference optimization, post-training pipelines, evaluation suites, and golden datasets. Analyze production traces and failures to create training data and targeted improvements. Collaborate on model routing, distillation, inference cost, and latency while determining when fine-tuning outperforms prompting, retrieval, or scaffolding.
Summary Generated by Built In
AI Researcher @ Varick AgentsABOUT VARICK:

Varick builds AI agents that take over real operational workflows inside the world's largest enterprises. Our forward-deployed engineers and strategists embed with client teams to map how work gets done and build POCs, then our engineers bring those agents into production. We're venture-backed, revenue-generating from day one, and already in production inside several of these companies. You'll join an elite team from Meta, AWS Bedrock, Citadel Securities, McKinsey, BCG, Stanford, and more.

ABOUT THE ROLE:

You make our agents better at the actual work. You own the post-training loop: taking real workflow data from our deployments and turning it into models and evals that push agent accuracy where a demo-grade model fails on the tenth run.

This is applied, production-facing research. Your results ship to agents running inside the world's largest enterprises.

WHAT YOU'LL DO:
  • 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, and cost and latency budgets

  • Keep a clear view of where fine-tuning earns its keep versus better prompting, retrieval, or scaffolding

WHAT WE'RE LOOKING FOR:
  • 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

HELPFUL EXPERIENCE:
  • Published research or open-source work in LLMs and agents

  • Agentic evals, tool-use training, or long-context work

  • Reinforcement learning environments

  • Taking research into production

CULTURE:
  • Extreme ownership. You own the tasks end-to-end, at an extreme level. You think beyond the task at hand, consider upstream and downstream impact, and are accountable for the entire outcome

  • An instinctual sense of urgency. You thrive in and help foster an environment with urgency. When a new request comes in, it is instinctually clear to you how that fits into the existing prioritization list. If it goes to the top of the list, you jump on it immediately, help the rest of the team realize the shift, and consider any impact of de-prioritizing other items

  • Low ego, high agency. We are all on this rocket ship together. If you win, the team wins and vice versa. You don't wait for permission to pursue a task. If you can do something that moves the needle, do it

  • Curious. Our work requires deep curiosity, both when it comes to understanding the clients' work and what matters to them, and when it comes to keeping up with and adopting the latest AI developments. Examples to showcase this could be pursuing niche interests, reading textbooks on the weekend, questioning the latest research, etc.

  • Perfectionist. You must be proud of your work product. In a client-facing business, it is critical that we put correct, well-informed, and well-documented material in front of the client. We are held to the highest standards and embody that mindset internally within Varick too

  • Startup mindset. Things are ambiguous. The hours are long. Weekend work is expected. And this excites you. With AI, the world is moving extremely quickly, and the ways of working are changing every day. We are at the forefront and help facilitate this rapid change. In the next 3, 5, and 10 years, things are going to change significantly, and you join to work hard and be a part of this exciting journey towards an AI-native future

  • Feedback metabolism. You never need the same correction twice, and you treat every review as compounding rather than criticism. You spot opportunities to give, and are comfortable giving, feedback to all of your peers, be it upward, downward, or horizontal

  • Competitive. You are here to win. You are driven by performing better than competitors, and by delivering outcomes that move the needle. Your background speaks to your competitive mindset with top performance in e.g., sports, eSports, former career, research, etc.

  • Based in SF or willing to relocate. On-site in our Financial District office, five days a week

WHAT THIS ROLE ISN'T:
  • Not research for its own sake. Every result is measured against real workflows

  • Not client-embedded. Our forward deployed team works on client sites; you work on the models behind them

WHY VARICK:
  • Real data. Production traces from agents doing real work at some of the world's largest companies

  • Your work ships. A better model shows up in client results within weeks

  • High-bar team. Everyone here is someone you'll want to learn from: operators, engineers, and strategists at the top of their craft

  • Small, early team. Your judgment shapes the product, the playbooks, and how Varick scales

  • Meaningful equity at a stage where it still compounds

We are an equal opportunity employer. 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.

Skills Required

  • Strong applied machine learning background with hands-on post-training experience using LLMs, such as SFT, RLHF, DPO, or similar
  • Experience taking at least one model or system to real users and explaining its production behavior
  • Fluency with the modern model training stack and understanding of inference economics
  • Strong evaluation discipline and ability to rely on measured results
  • Published research or open-source work in LLMs and agents
  • Experience with agentic evaluations, tool-use training, or long-context systems
  • Experience with reinforcement learning environments
  • Experience taking research into production
  • Based in San Francisco or willing to relocate
  • Ability to work onsite five days per week in the Financial District office
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The Company
12 Employees
Year Founded: 2025

What We Do

Varick Agents builds custom AI agent systems for billion-dollar enterprises, embedding production agents into existing tools such as Salesforce, NetSuite, ServiceNow, and Workday. Its solutions handle exception management, cross-system reconciliation, judgment-based routing, Tier-2 tickets and claims, month-end close, and audit-grade compliance. Rather than automating isolated tasks, the company redesigns and executes complete departmental workflows from intake through reporting, helping modernize complex enterprise operations end to end.

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