Python · TensorFlow · PyTorch · Keras · NLTK
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.