# Forward Deployed Engineer, Tinker

**Company:** [Thinking Machines Lab](https://hotfix.jobs/companies/thinking-machines-lab)
**Location:** San Francisco, CA
**Role:** Developer Relations
**Salary:** $350k – $475k/yr
**Skills:** llm fine-tuning, supervised learning, Reinforcement Learning, hyperparameter tuning, open-source libraries, developer experience, Model Training, model inference, Python
**Posted:** 2026-07-15

> Research Engineer focused on developer experience for Tinker, Thinking Machines' fine-tuning API. Build cookbook recipes, prototypes, integrations, and tools while working directly with users to improve onboarding, debug issues, and guide product priorities for LLM customization.

## Job Description

## What You’ll Do
- Write, test, and maintain high-quality code examples, recipes, and documentation in the Tinker cookbook.
- Work directly with a variety of users to debug technical issues and optimize their fine-tuning pipelines.
- Partner with internal research and infra teams to identify and prioritize improvements to Tinker’s developer experience.
- Build tools, integrations, and demos that reduce user friction and make it easy to experiment and deploy models.
- Share insights from the field through posts, guides, and talks.
- Help develop Tinker’s near-term roadmap and long-term product strategy.

## Preferred Qualifications
- Experienced with fine-tuning large language models (LLMs) with supervised and reinforcement learning, and how to tune the hyperparameters of these algorithms.
- Experienced with developing software libraries, especially open-source libraries.
- Thoughtful about developer experience, with clear ideas around developer ergonomics, onboarding flow, and sharp edges.
- Passionate about engaging with open-source communities, whether by contributing code, answering questions, or sharing learnings.
- Have written public or internal code examples, tutorials, or papers that helped others understand and apply technical concepts.
- Comfortable reasoning about model training, fine-tuning, and inference, and can credibly discuss tradeoffs between different approaches.

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