T
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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