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Senior Principal Software Engineer (Machine Learning)

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Onsite

Senior Principal Software Engineer (Machine Learning)

Toast

Senior Principal Software Engineer (Machine Learning)

Skills

KotlinDeep LearningData ScienceJavaKafkaDatabricksMachine LearningAI

2 months ago

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Important information


Contract type:

Permanent

Salary:

$222,000 – $453,000 / year

Location:

United States

Work mode:

Onsite

Published on:

13 August 2026

What they need


  • The Machine Learning Platform team builds and operates the core infrastructure that powers AI and 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 Senior Principal Software Engineer to act as the technical leader on the ML Platform team, shaping the systems that will carry Toast’s AI and 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
  • 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

Benefits

  • Peer and company recognition programs
  • Unlimited Vacation
  • Sabbatical opportunity after five years
  • Professional Development Reimbursement Program
  • Commitment to Employee Wellness through resources such as a quarterly Wellness Stipend
  • Various peer and company recognition programs
  • 401(k) and matching
  • Medical, Dental, & Vision Coverage
  • Mental Health Benefits
  • Subsidized backup childcare- 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
  • The ideal candidate is a domain expert with considerable ML platform experience, who partners with ML engineers, data scientists, product, and infrastructure leadership on high-leverage opportunities
  • 10+ 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
  • Deep proficiency with distributed systems concepts: consistency, latency, throughput, fault tolerance, and observability
  • Demonstrated technical leadership, with ability to drive cross-team alignment and influence engineering, product, and business stakeholders
  • Strong understanding of data modeling, query languages, and the online/offline data patterns that underpin ML systems
  • Mastery of a modern backend language, ideally Java or Kotlin
  • Bachelor’s degree in Computer Science or a related field, or equivalent practical experience
  • Hands-on experience with open-source or commercial ML platform components (e.g. Tecton, MLflow, SageMaker, Databricks)
  • Experience serving LLMs or deep-learning models in production, including GPU capacity planning and inference optimization
  • Experience building or operating experimentation / A-B testing platforms at scale
  • Familiarity with real-time streaming systems (Kafka, Flink, Spark Streaming) and their use in feature computation
  • Prior work supporting internal-developer-facing platforms with a product mindset

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