Staff Engineer (ML Platform)

TL;DR

Staff Engineer (ML Infrastructure): Designing and evolving the infrastructure that powers Stripe's ML-driven products with an accent on scalability, low latency, and robust system design. Focus on building next-generation ML training and serving systems, LLM orchestration, and high-performance feature stores.

Location: Toronto

Salary: CA$208,000 - CA$312,000

Company

Stripe is a financial infrastructure platform for businesses, providing tools to accept payments and grow revenue globally.

What you will do

• Lead end-to-end architecture and system design for complex projects across the ML Platform.
• Define technical direction for ML workflow orchestration and scalable CPU/GPU compute infrastructure.
• Design systems for LLM fine-tuning, low-latency model inference, and large-scale feature stores.
• Collaborate cross-functionally with data scientists, product teams, and senior leadership to increase ML impact.
• Drive company-wide initiatives to improve MLOps maturity and ML development velocity.
• Mentor and grow other engineers, serving as a role model for software system design and operation.

Requirements

• 10+ years of professional software development experience with a background in service-oriented architecture and large-scale distributed systems.
• Proven track record as a technical lead, managing multi-team initiatives and mentoring members.
• Experience building and operating production ML platforms (training, serving, orchestration, or data systems).
• Strong communication skills and ability to explain complex concepts to diverse stakeholders.
• Ability to operate with high autonomy in ambiguous environments.
• Hands-on experience using AI tools to accelerate professional workflows.

Nice to have

• Experience with distributed ML training, model registries, and experiment tracking.
• Familiarity with LLMs, RAG, and agentic AI patterns (e.g., multi-agent orchestration).
• Experience with cloud services like AWS, SageMaker, Bedrock, Databricks, or OpenAI.
• History of training and shipping ML models to production to solve critical business problems.

Culture & Benefits

• Opportunity to work on a platform processing over $1.9T in annual payment volume.
• High level of autonomy and responsibility in shaping the global economy's infrastructure.
• Collaborative environment working with geographically distributed teams.
• Focus on technical excellence and creative problem-solving.