About the role

What you’ll do

• Lead model selection and customization for AI capabilities
• Own the strategy and roadmap for a shared customization platform
• Partner with teams to launch tuned models
• Establish evaluation and governance practices
• Explain trade-offs clearly to senior leaders
• Run experiments and review training and evaluation results

What you’ll need

• 7+ years across product management, engineering, or applied ML, including 3+ years as PM on AI/ML, data, or infrastructure platforms
• Hands-on experience customizing and shipping open-weight models in production via fine-tuning, parameter-efficient tuning, preference optimization, distillation, or quantization
• Deep knowledge of prompt/context engineering and RAG, plus experience selecting models with quality, cost, latency, hardware, and licensing trade-offs
• Strong evaluation skills including task-specific evals, avoiding train/test leakage, and distinguishing real improvement from noise
• Working knowledge of PyTorch, Hugging Face, and inference servers such as vLLM or SGLang, plus GPU cost/capacity trade-offs and comfort writing Python
• Ability to lead without authority across engineering, science, infrastructure, and business teams; bonus experience with RLHF, reward modeling, synthetic data, agentic fine-tuning, model governance, private/hybrid deployments, or regulated industries

Original job description