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. Toast powers restaurants of every size, from neighborhood favorites to the largest multi-unit brands in the industry. We're investing in Vision AI to help operators see more of what's happening in their restaurants and act on it, so they can run more efficiently, provide better service, and become even more profitable.

We're looking for a Principal Machine Learning Engineer, Vision AI to architect and build the platform behind these products. You'll have an outsized influence on how Vision AI is built and how it reaches restaurants across Toast. This is a highly hands-on role: you'll spend most of your time designing, building, and shipping, and you'll own the technical direction of the platform.

A day in the life (Responsibilities)

Architect and build the core platform. Design the video pipeline and other essential components, from how video is captured and ingested to how it's stored, processed, and delivered, so the platform is secure and reliable.
Own the services that analyze video. Build the services that process footage, run vision models, and turn what they see into observations and metrics restaurants can act on.
Build ML systems and drive model choices. Select and integrate vision models, including vision-language models, and build the ML systems around them, such as inference pipelines, evaluation, and monitoring of model performance in production. Partner with our ML engineers as models mature.
Build customer-facing use cases across Toast. Bring vision-powered features to restaurant operators across Toast's products and surfaces.
Set the technical direction. Make the key architecture decisions for the Vision AI platform, building what's right for today while keeping a clear path to where we're headed.
Stay hands-on. Write production code, prototype new ideas, and ship. You'll lead through building, not just reviewing.
Collaborate across orgs. Partner with teams across Toast on integrations, dependencies, and working models when the work calls for it.
Lead with AI-forward engineering. Use AI coding tools and agent harnesses as a core part of how you design, build, test, and ship, and help the team adopt workflows that make everyone faster.
Raise the bar. Mentor engineers, set standards for reliability, security, and privacy, and make the platform easier to build on with every use case.

What you'll need to thrive (Requirements)

Deep engineering experience. 10+ years building software, including designing and shipping distributed systems and data pipelines in production at scale.
Backend and cloud expertise. Strong experience with cloud infrastructure (AWS preferred), event-driven systems (e.g. Pulsar or Kafka), storage, and APIs, in Kotlin, Java, or similar languages, plus comfort with Python.
Experience with ML systems and video. You don't need to be a model researcher, but you understand how models are trained, tuned, and served, and you've built and operated ML systems in production, including inference pipelines, evaluation, and model monitoring. You're also comfortable working with video (streams, codecs, tools like ffmpeg).
Pragmatic architecture. A track record of designing systems that fit the current stage, avoiding over-engineering while leaving room to grow.
Zero-to-one experience. Experience taking ambiguous ideas from prototype to production with real customers.
Hands-on keyboard. You still write significant production code and enjoy doing it.
AI-forward ways of working. Daily use of AI coding assistants and agent harnesses (e.g. Claude Code, Cursor), and experience building with LLM or vision-language model APIs.
Collaboration and communication. Ability to align engineers, product partners, and other orgs around technical decisions and explain tradeoffs clearly.

This role may not be a fit for you if...

You want a primarily cross-org or architecture-only role. This is a hands-on keyboard role. You'll spend most of your time building, with cross-org collaboration when the work requires it.