Software Engineer, ML Infra

NewsBreak•Mountain View, CA

About The Position

We’re hiring a Machine Learning Infrastructure Engineer to help build the backbone that trains, serves, and monitors the models behind our Ads and Recommendations products. You’ll join a small, high-ownership team that ships platform improvements end-to-end—partnering with product and data teams, reducing latency and cost, and shortening the path from an idea to a safely launched model. You’ll work across the ML lifecycle: making training faster and more reliable, improving model serving performance, and strengthening our feature/embedding platform so models stay fresh and consistent between offline and online use. We’re looking for someone who can take real ownership, finish what’s started, and raise the bar on stability and developer experience. Why this role Scope & impact: small team, big surface area—your work lands directly in production. Ownership: from design to rollout to post-launch learnings; real autonomy with support. Growth: visibility across the stack and a clear path to lead projects and mentor others. Pragmatic culture: we optimize for outcomes over buzzwords, and we value clear thinking, and follow-through. If you like building reliable systems that make ML teams move faster—and you enjoy turning complexity into simple, durable solutions—we’d love to talk.

Requirements

Education: Bachelor's degree in a related field with 5+ years of relevant experience, or MS/PhD in a related field with 3+ years of relevant experience
Experience delivering production systems that support ML use cases (training or serving).
Proven track record in building and maintaining large-scale distributed backend systems
Proficient in Python, with a strong understanding of object-oriented languages such as C++ or Java
Strong problem solving skills with good teamwork and communication skills

Nice To Haves

Experience leading cross-team projects from design through stable rollout.
Experience iterating on ML models and shipping them to production.

Responsibilities

Design and develop machine learning infrastructure.
Own and enhance core components of the ML infrastructure, including systems for offline and online model training, model pipeline health monitoring, model serving, feature authoring, and feature serving.
Proactively address ML infrastructure issues that may impact production.
Collaborate with ML engineers to build robust model pipelines utilizing the ML infrastructure.