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About the Role
Chakra Labs is building high-fidelity environments, evaluation systems, and datasets for frontier AI research, collaborating with leading AI labs. The role focuses on improving AI models through post-training loops, environment and task design, creating high-fidelity trajectories, developing reward and evaluation systems, building training and research infrastructure, and addressing customer research problems.
Responsibilities
- Design and run model improvement workflows across supervised fine-tuning, preference optimization, and reinforcement learning approaches.
- Design tasks, tools, validators, reward signals, and evaluation harnesses for agent environments.
- Create, inspect, and improve data that teaches models how to behave, focusing on taste, correctness, and edge cases.
- Develop reward functions, rubrics, validators, and analysis tools to generate training signals from model behavior.
- Run experiments on distributed GPU clusters using PyTorch and FSDP, and build supporting infrastructure.
- Translate ambiguous research goals from AI labs into concrete environments, datasets, experiments, and deliverables.
Requirements
- Masters/PhD-level knowledge of machine learning fundamentals, including linear algebra, optimization, and stochastic gradient descent.
- Hands-on experience with or deep understanding of LLM fine-tuning, preference data, reward modeling, or reinforcement learning for language models.
- Excitement for agent environments, multi-turn tool use, sandboxed tasks, and eval harnesses.
- Strong proficiency in Python and comfort building with PyTorch, FastAPI, and modern ML infrastructure.
- Ability to work at a high level and drop into lower-level primitives when necessary.
- Strong engineering judgment, balancing research ambiguity with production constraints.
- Ability to iterate quickly, be rigorous, and identify fragile results.
- Experience is flexible, with a general expectation of 3-5 years.
Skills
- Machine learning fundamentals
- Linear algebra
- Optimization
- Stochastic gradient descent
- LLM fine-tuning
- Preference data
- Reward modeling
- Reinforcement learning for language models
- Agent environments
- Multi-turn tool use
- Sandboxed tasks
- Eval harnesses
- Python
- PyTorch
- FastAPI
- ML infrastructure
- Distributed GPU clusters
- FSDP
Location
- Remote
Work Type
- Full-time
Experience Level
- Mid-level
- Senior
Education Level
- Masters
- PhD
About the Company
- Chakra Labs' mission is to encode human taste into intelligence.
- We build high-fidelity environments, evals, and datasets for frontier AI research, working with several of the top labs.
- Our work sits at the frontier of post-training, agent environments, data quality, and research infrastructure.
- We care about building systems that make models better in ways that are measurable, useful, and hard to fake.
- The team is composed of individuals with experience from companies like Stripe, Snap, AWS, Microsoft, and Airtable, bringing years of shipping high-impact products.