• Experience building and shipping ML systems used in production, not just research prototypes.
• Strong understanding of modern large-model training, fine-tuning, evaluation, and inference.
• Strong software engineering and systems fundamentals.
• Experience operating ML workloads at meaningful scale, particularly GPU-based systems.
• Strong technical judgment and the ability to navigate ambiguous problems independently.
• A bias toward experimentation, measurement, and shipping.
• High standards for correctness, reliability, and production quality.
• Outcomes
• Outcomes
• Research and models reliably translate into production-ready solutions with clear performance and quality targets.
• ML pipelines, training loops, and inference systems are stable, efficient, and maintainable.
• Production issues are detected, debugged, and resolved quickly, minimizing user impact.
• Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction.
• Iterations on models and systems are measurable, safe, and improve user experience over time.
• GPU-based training and inference system
• You have built or shipped real ML systems used by people, not just demos.
• You are comfortable working with large models and understanding their failure modes.
• You write strong, production-grade code and care about system correctness.
• How We Work
• How We Work
• We are a small, high-talent-density, hands-on team. Engineers have broad ownership and are expected to exercise strong judgment and execute independently.
• We make decisions quickly, work closely together, and balance speed with engineering fundamentals. We care less about process and more about building something exceptional.