Member of Technical Staff, Applied Research

Embedding VC; New York (NY); On-site; USD 140,000 - 210,000; Full time

Job summary

Chakra Labs in New York City is seeking a focused ML engineer to advance post-training loops, environment design, and high-fidelity data collection. You will help shape model improvement workflows across supervised fine-tuning, preference data, and reinforcement learning approaches, contributing to meaningful signals that actually improve models.

The role blends research curiosity with production pragmatism, with responsibilities spanning training infrastructure, evaluation harnesses, and

Qualifications

• Masters or PhD-level knowledge of machine learning fundamentals
• Hands-on post-training experience with LLM fine-tuning, preference data, or reinforcement learning for language models
• Environment-building instinct for agent environments, eval harnesses, and production-ready tools
• Python and PyTorch fluency; ability to work across ML infra like FastAPI

Responsibilities

• Design and run post-training loops across supervised fine-tuning, preference optimization, and reinforcement learning for language models
• Build environments and tasks, validators, reward signals, and evaluation harnesses that test meaningful capabilities
• Create, inspect, and improve data trajectories for training frontier models
• Develop reward functions, rubrics, validators, and analysis tools to turn model behavior into useful signals
• Contribute to distributed GPU training, PyTorch/FSDP, and ML infrastructure for training and data generation
• Translate ambiguous research goals into concrete environments, datasets, experiments and deliverables

Job description

About Us

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.

What You’d Work On

• Post-training loops. You’d help design and run model improvement workflows across supervised fine-tuning, preference optimization, and reinforcement learning approaches like GRPO. The work is not just launching training jobs; it’s figuring out what signal matters, how to collect it, and whether the model actually improved.

• Environment and task design. We build environments that feel real and scenarios that push agents past static benchmark behavior. You’d design tasks, tools, validators, reward signals, and evaluation harnesses that test meaningful capabilities instead of whatever is easiest to measure.

• High-fidelity trajectories. You’d create, inspect, and improve the data that teaches models how to behave. That means caring about taste, correctness, edge cases, and whether a trajectory would actually help a frontier model learn.

• Reward and evaluation systems. You’d work on reward functions, rubrics, validators, and analysis tools that turn messy model behavior into useful training signal. You should be interested in where evals lie, where rewards get hacked, and how to make measurements more robust.

• Training and research infrastructure. You’d run experiments across distributed GPU clusters, work with PyTorch and FSDP, and build the infrastructure needed to support model training, evaluation, and data generation at scale.

• Customer research problems. You’d work with frontier AI labs to translate ambiguous research goals into concrete environments, datasets, experiments, and deliverables.

About You

• Machine learning fundamentals. You have Masters / PhD-level knowledge of machine learning fundamentals. You understand linear algebra, optimization, stochastic gradient descent, and can reason from first principles when a model or training run behaves unexpectedly.

• Hands-on post-training experience. You have worked with or deeply understand LLM fine-tuning, preference data, reward modeling, or reinforcement learning for language models. You know the difference between reproducing a recipe and understanding why it works.

• Environment-building instinct. You are excited by agent environments, multi-turn tool use, sandboxed tasks, eval harnesses, and the question of how to test capabilities that do not fit neatly into a benchmark.

• Python and PyTorch fluency. You are strong in Python and comfortable building with PyTorch, FastAPI, and modern ML infrastructure. You can work at a high level, but you are also comfortable dropping into lower-level primitives when the abstraction leaks.

• Strong engineering judgment. You can move between research ambiguity and production constraints. You know when to iterate quickly, when to be rigorous, and when a result is too fragile to trust.

• Experience. No hard rule. Roughly 3-5 years is what we imagine, but more or less experience works if the expectations above resonate with you.

What Makes This Different

• The work is concrete. You are not just “touching the latest AI stack.” You are building the environments, trajectories, evals, rewards, and training loops that frontier labs use to improve models.

• It is research-facing, but production-minded. Our customers are AI researchers and labs pushing the edge of what agents can do. The systems you build need to support real experiments, real users, and real deadlines.

• Ownership, not theater. You own whole problems, not isolated tickets. One week you might be designing a new environment; the next you might be debugging a training run, improving a reward function, or scaling an eval pipeline.

• Ambiguity is part of the job. There is no fixed playbook for this work. Data, post-training, and agent evaluation are changing quickly. If you need every problem to be fully specified before you begin, this role will be challenging.

• The team. Our team is ex-Stripe, Snap, AWS, Microsoft, Airtable — you'll work with a small team who has years of shipping high-impact products over the last decade.