Staff Machine Learning Engineer, Toast

• 8+ years delivering complex backend or infrastructure systems at scale
• Direct experience building or operating core ML infrastructure — feature stores, model serving, experimentation platforms, training orchestration, or equivalent
• Mastery of a modern backend language such as Python, Java, Kotlin, Go, or Scala
• Deep proficiency with distributed systems concepts: consistency, latency, throughput, fault tolerance, and observability
• Strong understanding of data modeling, query languages, and the online/offline data patterns that underpin ML systems
• Demonstrated technical leadership, with ability to drive cross-team alignment and influence engineering, product, and business stakeholders
• Bachelor's degree in Computer Science or a related field, or equivalent practical experience

Desirable

• The Machine Learning Platform team builds and operates the core infrastructure that powers ML across Toast — 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
• Toast is seeking a Staff Software Engineer to act as a technical leader on the ML Platform team, shaping the systems that will carry Toast'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
• This position suits an engineer comfortable writing production code, leading technical design for distributed systems, and influencing organizational decisions about how Toast builds and deploys ML
• 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
• Lead design and delivery of large-scope platform initiatives from conception through production, coordinating across ML, data, and infrastructure teams
• Identify and resolve systemic technical challenges: online/offline feature parity, model deployment friction, experimentation velocity, GPU utilization, cross-team dependencies
• Set and maintain a high engineering quality bar through hands-on code contributions, design reviews, and mentorship of platform and ML-adjacent engineers
• Partner with ML engineering, data science, product, and platform leadership to translate ML strategy into technical roadmaps
• Define the paved paths ML teams use to ship models safely — from feature registration through canary rollout, monitoring, and rollback
• Leverage AI-augmented development tools to increase development velocity and code quality