Member of Technical Staff, Research Engineering

About the Role Join a small AI research engineering team developing post-training methods and infrastructure for production AI agents. You will work across research and production engineering to improve model quality, cost, and latency in real-world applications. What You'll Do Build evaluation suites, graders, and data pipelines from production workflows and traces. Own post-training work in customer settings, including data preparation, 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 hands-on engineering experience at an AI startup or in an ML research group, working with LLM post-training, evaluation, or inference. Strong Python programming skills and solid foundations in machine learning and computer science. Practical experience training, evaluating, and serving LLMs, beyond using model APIs or prompt and agent wrappers. Hands-on experience with tools and technologies such as PyTorch, JAX, LLM training, evaluation harnesses, model serving, vLLM, SGLang, Megatron, or reinforcement learning environments. A shipped system, substantial project, research contribution, internship, or open-source work demonstrating technical depth and personal ownership. Ability to learn unfamiliar systems, debug independently, communicate technical decisions clearly, and measure whether solutions work. Interest in post-training or inference, with experience across multiple parts of the AI and ML stack. 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, United States.