Member of Technical Staff, Applied Research

The role

You will design and run model improvement workflows, including supervised fine-tuning, preference optimization, and reinforcement learning. You will also build environments, tasks, reward systems, and research infrastructure for frontier AI labs.

What you will do

• Design and run model improvement workflows across supervised fine-tuning, preference optimization, and reinforcement learning.
• Design tasks, tools, validators, reward signals, and evaluation harnesses.
• Create, inspect, and improve data for teaching models how to behave.
• Work on reward functions, rubrics, validators, and analysis tools.
• Run experiments across distributed GPU clusters.
• Work with PyTorch and FSDP.
• Build infrastructure for model training, evaluation, and data generation at scale.
• Translate ambiguous research goals into concrete environments, datasets, experiments, and deliverables.

Required qualifications

• Masters / PhD-level knowledge of machine learning fundamentals.
• Understand linear algebra, optimization, and stochastic gradient descent.
• Worked with or deeply understand LLM fine-tuning, preference data, reward modeling, or reinforcement learning for language models.
• Strong in Python.
• Comfortable building with PyTorch and FastAPI.
• Comfortable with modern ML infrastructure.

Nice to have

• Roughly 3-5 years of experience

About the company

Chakra Labs builds high-fidelity environments, evals, and datasets for frontier AI research. The team consists of ex-Stripe, Snap, AWS, Microsoft, and Airtable members.