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Research, Finetuning Science
Summary by AIEngineer.careers
Research, Finetuning Science is a AI Research Engineer role (part-time). with Thinking Machines Lab. in SAN FRANCISCO, US. Compensation shown: $350K–$475K. Imported listing (source: emploive.com). Apply on the employer's site (emploive.com).
Imported listing (source: emploive.com) · Apply on emploive.com
SAN FRANCISCO · US
$350K–$475K
Part-time
MID
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from emploive.com
Thinking Machines Lab is seeking an AI Research Engineer to advance frontier customization techniques and build the post-training engine 'Tinker'. The role focuses on fine-tuning science, including LoRA and parameter-efficient methods, while integrating research findings into product defaults and open-source recipes. You will collaborate with internal teams to improve stability and efficiency of large-scale RL runs.
Responsibilities
- Advance the science of fine-tuning and frontier post-training techniques.
- Contribute to LoRA and parameter-efficient fine-tuning to push customization quality and reliability.
- Ship research into product by informing Tinker's training defaults and primitives.
- Improve stability, efficiency, and reliability of large-scale fine-tuning and RL runs.
- Share findings through papers, technical blog posts, and community contributions.
Requirements
- Bachelor’s degree or equivalent experience in Computer Science, ML, Physics, or Mathematics.
- Proficiency in Python and familiarity with deep learning frameworks (PyTorch, TensorFlow, or JAX).
- Comfort debugging distributed training and writing code that scales.
- Strong theoretical and empirical grounding in ML fundamentals.
Thinking Machines Lab
San Francisco · US · 135+ employees
Thinking Machines Lab is an American artificial intelligence startup founded by former OpenAI CTO Mira Murati. The company builds open-weights large language models and provides research infrastructure to make AI more customizable and accessible for unique needs in scientific and programming domains.
