<p><a href="https://himalayas.app/companies/toasttab">Toast</a> creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.</p><p>The Machine Learning Platform team builds and operates the core infrastructure that powers ML across <a href="https://himalayas.app/companies/toasttab">Toast</a> — the feature store, model hosting and serving, the experimentation platform, training pipelines, and the tooling ML engineers and data scientists rely on every day. Our work directly enables the models that drive personalization, forecasting, fraud detection, search, and the growing set of AI-powered experiences shipping to restaurants.</p><p><a href="https://himalayas.app/companies/toasttab">Toast</a> is seeking a Staff Software Engineer to act as a technical leader on the ML Platform team, shaping the systems that will carry <a href="https://himalayas.app/companies/toasttab">Toast</a>'s ML capabilities into the next decade. The role involves driving architectural direction across the platform, delivering foundational infrastructure that other teams build on, and elevating fellow engineers. The ideal candidate is a domain expert who partners with ML engineers, data scientists, product, and infrastructure leadership on high-leverage opportunities.</p><p>This position suits an engineer comfortable writing production code, leading technical design for distributed systems, and influencing organizational decisions about how <a href="https://himalayas.app/companies/toasttab">Toast</a> builds and deploys ML.</p><h3>A day in the life (Responsibilities)</h3><ul><li>Own technical direction of the ML Platform — feature store, model hosting and serving, experimentation, training infrastructure — driving architectural decisions around scalability, reliability, latency, and cost</li><li>Lead design and delivery of large-scope platform initiatives from conception through production, coordinating across ML, data, and infrastructure teams</li><li>Identify and resolve systemic technical challenges: online/offline feature parity, model deployment friction, experimentation velocity, GPU utilization, cross-team dependencies</li><li>Set and maintain a high engineering quality bar through hands-on code contributions, design reviews, and mentorship of platform and ML-adjacent engineers</li><li>Partner with ML engineering, data science, product, and platform leadership to translate ML strategy into technical roadmaps</li><li>Define the paved paths ML teams use to ship models safely — from feature registration through canary rollout, monitoring, and rollback</li><li>Leverage AI-augmented development tools to increase development velocity and code quality</li></ul><h3>What you'll need to thrive (Requirements):</h3><ul><li>8+ years delivering complex backend or infrastructure systems at scale</li><li>Direct experience building or operating core ML infrastructure — fe