Quick overview
Job description
About the Role
Join an early-stage AI company developing specialized models and learning systems for production agents. You will work across research and production engineering, building post-training methods and infrastructure that improve model quality, cost, and latency in real-world applications.
What You'll Do
Build evaluation suites, graders, and data pipelines using production workflows and traces.
Own post-training projects in customer settings, including data work, evaluations, reinforcement learning environments, and training runs.
Implement agent environments and harnesses for reproducible experiments and training data generation.
Run and analyze post-training experiments, reproduce promising research, and debug related systems.
Turn successful experiments into reliable components of a post-training and continual learning platform.
What We're Looking For
One to five years of engineering experience at an AI startup or in an ML research group, working hands-on with LLM post-training, evaluation, or inference.
Strong Python programming skills and solid foundations in machine learning and computer science.
Hands-on experience training, evaluating, and serving LLMs, beyond using APIs or prompt and agent wrappers.
Experience with tools and technologies such as PyTorch, JAX, reinforcement learning, agent environments, evaluation harnesses, vLLM, SGLang, or Megatron.
Evidence of building and owning technical work, such as shipped systems, engineering internships, open-source contributions, research, or substantial projects.
Ability to learn unfamiliar systems, debug independently, communicate technical decisions, and measure whether solutions work.
Compensation & Benefits
Base salary: $200,000 to $300,000 annually. Visa sponsorship is available.
Location
On-site, five days per week in Mountain View, California.