Requirements

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

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.
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.
hands-on team. Engineers have broad ownership and are expected to exercise strong judgment and execute independently.
and balance speed with engineering fundamentals. We care less about process and more about building something exceptional.
If there appears to be a fit
but no more than 4 interviews.
Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.
so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for
we'll extend an offer to join us. This isn't just a job offer
it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.

Principal Machine Learning Engineer

Summary