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Home/Remote AI & Machine Learning Jobs/Toast/Staff Machine Learning Engineer
Toast

Staff Machine Learning Engineer

Toast

Remote, USFull-time$151k - $309kPosted about 24 hours ago
AI / Machine LearningSoftware Development

Summary

Toast is hiring a Staff Machine Learning Engineer to join their AI / Machine Learning team. Toast 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. Key skills: Python, Go, Kubernetes, Java, Kotlin.

Toast 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.

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.

A day in the life (Responsibilities)

  • 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

What you'll need to thrive (Requirements):

  • 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

Nice to Haves:

  • Hands-on experience with open-source or commercial ML platform components (e.g. Tecton, MLflow, SageMaker, Databricks)
  • 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
  • Experience serving LLMs or large deep-learning models in production, including GPU capacity planning and inference optimization
  • Comfort with Kubernetes and modern cloud-native infrastructure
  • Prior work supporting internal-developer-facing platforms with a product mindset

AI at Toast

At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.

Our Total Rewards Philosophy 
We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits.

#LI-REMOTE

The base salary range for this role is listed below. The starting salary will be determined based on skills, experience, and geographic location. In addition to base salary, our total rewards components include cash compensation (overtime, bonus/commissions if eligible), equity, and benefits. You can learn more about how we align pay with local labor markets in our Geographic Pay Zone Philosophy.

Zone A
$193,000—$309,000 USD
Zone B
$168,000—$269,000 USD
Zone C
$151,000—$242,000 USD

How Toast Uses AI in its Hiring Process

Throughout the hiring process, our goal is to get to know you. We use AI tools to support our recruiters and interviewers with tasks like note-taking, summarization, and documentation of interviews to ensure they can be fully focused on your conversation. All hiring decisions are made by people. To learn more: https://careers.toasttab.com/ai-in-hiring

Our Approach to Hybrid Working

We embrace a hybrid work model that fosters in-person collaboration while valuing individual needs. Our goal is to build a strong culture of connection as we work together to empower the hospitality community, regardless of location. Please visit the Locations page on our career site to learn more about our in-office expectations by region: https://careers.toasttab.com/locations-toast

Diversity, Equity, and Inclusion is Baked into our Recipe for Success

At Toast, our employees are our secret ingredient—when they thrive, we thrive. The restaurant industry is one of the most diverse, and we embrace that diversity with authenticity, inclusivity, respect, and humility. By embedding these principles into our culture and design, we create equitable opportunities for all and raise the bar in delivering exceptional experiences.

We Thrive Together

We embrace a hybrid work model that fosters in-person collaboration while valuing individual needs. Our goal is to build a strong culture of connection as we work together to empower the restaurant community. To learn more about how we work globally and regionally, check out: https://careers.toasttab.com/locations-toast.

Apply today!

Toast is committed to creating an accessible and inclusive hiring process. As part of this commitment, we strive to provide reasonable accommodations for persons with disabilities to enable them to access the hiring process. If you need an accommodation to access the job application or interview process, please contact candidateaccommodations@toasttab.com.

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For roles in the United States, it is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

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