Senior Machine Learning Engineer

About the Role

As a Senior Member of Technical Staff, Machine Learning, you will independently own critical ML subsystems in production. In this hands-on, high-impact role, you will take ambiguous and complex problems, design practical and scalable solutions, and ship systems that operate reliably at scale under real-world production constraints such as latency, cost, reliability, and safety.

Core Focus & Responsibilities

• End-to-End Ownership: Own ML subsystems across the full lifecycle, including data preparation, training, evaluation, inference, deployment, and continuous iteration.
• Research to Production: Translate research ideas and experimental approaches into robust, scalable systems that perform reliably in production.
• Production Debugging: Diagnose model failures, system bottlenecks, and performance issues using real-world production signals and data.
• Rapid Iteration: Ship improvements quickly, measure their impact using production metrics, learn from the results, and continuously refine the system.
• Cross-Functional Collaboration: Partner closely with research, product, and engineering teams to turn ML capabilities into measurable user impact.
• Technical Leadership & Mentorship: Mentor other ML engineers, provide thoughtful technical reviews, and raise engineering standards through strong technical judgment and leadership by example.

What We’re Looking For

• Production Track Record: Proven experience building, deploying, and maintaining ML systems used by real-world users.
• Deep Model Intuition: Strong understanding of how modern ML models behave, fail, and evolve in real-world production environments.
• Systems Mindset: Ability to write high-quality, production-ready code and design scalable systems rather than isolated scripts or experiments.
• Independent Execution: High degree of ownership, with the ability to work independently, bring structure to ambiguous problems, and drive complex projects through to completion.
• Communication & Growth: Strong communication skills, a fast-learning mindset, and a commitment to continuous improvement through experimentation and iteration.

Tech Stack

• Languages & Frameworks: Python, PyTorch / JAX
• Infrastructure: GPU-based training and inference systems

Expected Outcomes

• Target Achievement: Ensure ML models and systems consistently meet accuracy, latency, reliability, scalability, and efficiency targets in production.
• System Stability: Monitor, diagnose, and resolve complex production issues quickly while minimizing disruption to users.
• Robust Pipelines: Build and maintain scalable, reproducible, and maintainable training, inference, and data pipelines.
• Data-Driven Impact: Drive measurable improvements in ML models and systems based on real-world signals, production metrics, experimentation, and user feedback.
• Engineering Standards: Provide mentorship, technical guidance, and constructive feedback that elevates the overall ML engineering standard across the team.

How We Work & Application Process

We are a small, world-class team with a high talent density and a strong bias toward action. We operate at a rapid pace, make decisions collaboratively, and balance high-quality engineering craftsmanship with rapid learning, experimentation, and shipping.

• Interview Process: If there appears to be a mutual fit, we will schedule 3–4 interviews with members of the technical team, conducted virtually and/or on-site. We value transparency and efficiency and aim to make timely, well-informed decisions throughout the process.