About the role:

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.

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.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related field with 2+ years of relevant work experience; or a Master’s/PhD in a related discipline.
• Familiarity with software development processes, including version control, bug tracking, and design documentation.
• Proficient in Python, with a strong understanding of object-oriented languages such as C++ or Java
• Basic knowledge of Applied Machine Learning and experience with major Deep Learning frameworks, such as PyTorch and TensorFlow.

Preferred qualifications

• Familiarity with cloud services such as AWS, GCP, Azure, and others.
• Contributions to open-source machine learning or infrastructure tools.
• A systematic, data-driven problem-solving approach combined with strong communication skills.