• 3–5+ years of experience in machine learning, research engineering, or a related technical field.
• Strong Python skills and experience with ML frameworks such as PyTorch or JAX.
• Hands-on experience training, fine-tuning, or evaluating modern ML models.
• Strong understanding of experimental design, model evaluation, and quantitative analysis.
• Familiarity with agentic AI fundamentals, including common harnesses, Model Context Protocol, agent benchmarks, and security risks to AI agents.
• Experience in one or more of the following: reinforcement learning, NLP/LLMs, computer vision, or multimodal ML.
• Strong software engineering fundamentals and the ability to work independently on ambiguous technical problems.
• Experience with RLHF/RLAIF, reward modeling, policy optimization, or other model post-training techniques.
• Experience evaluating frontier language or multimodal models.
• Experience with adversarial evaluations, robustness testing, or AI safety.
• Experience with distributed training, cloud ML infrastructure, or large-scale ML systems.
• We don't expect candidates to have experience across every area above. We value deep ML expertise, strong experimental instincts, and the ability to quickly learn new techniques.
• deep ML expertise, strong experimental instincts, and the ability to quickly learn new techniques