Post-Training AI Research Engineer

Thinking Machines Lab Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 350,000 - 475,000; Full time

Benefits offered by this job

Health benefits

Dental benefits

Vision benefits

Unlimited PTO

Parental leave

Relocation support

Job summary

Thinking Machines Lab Inc. in San Francisco, CA, seeks a post-training researcher to bridge raw model intelligence with practical, safe, and collaborative AI. You will write high-performance code and digest technical reports, blending theory with hands-on experimentation to shape how AI learns.

You will develop and tune post-training recipes, evaluate metrics, debug configurations, scale methodologies, and publish research that advances the field for industry and academia.

Qualifications

• Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX).
• Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
• Clarity in communication, an ability to explain complex technical concepts in writing.

Responsibilities

• Develop and tune the recipe: iterate on post-training recipes, consisting of datasets, training stages, and hyperparameters; measure how recipe choices affect metrics.
• Iterate on evals: define evaluations, optimize them, and ensure numbers reflect meaningful progress.
• Debug and understand: investigate results that don’t make sense and improve training configurations.
• Scale and explore: scale existing methods and develop new ones; compare performance vs dataset size.
• Publish and present research that moves the community forward. Share code, datasets, and insights.

Skills

Python

PyTorch

TensorFlow

JAX

Education

Bachelor's degree or equivalent in CS/ML/Physics/Math

Job description

Thinking Machines Lab Inc. in San Francisco, CA, seeks a post-training researcher to bridge raw model intelligence with practical, safe, and collaborative AI. You will write high-performance code and digest technical reports, blending theory with hands-on experimentation to shape how AI learns.

You will develop and tune post-training recipes, evaluate metrics, debug configurations, scale methodologies, and publish research that advances the field for industry and academia.