Staff Software Engineer, Machine Learning Platform (AI)

Location: Remote in the United States, or office-based in Seattle or South San Francisco. Office-assigned employees spend at least 50% of each month in their local office or with users; remote employees regularly work from home and may attend meetings, on-sites, meet-ups, and events in an office.

Salary: $203,600–$305,400 annual US base salary, with the final range determined by experience, qualifications, and location. Additional compensation may include equity, bonuses, 401(k), medical, dental, vision, and wellness benefits.

Company

Stripe is a financial infrastructure platform that enables businesses to accept payments, grow revenue, and build new business opportunities.

What you will do

• Own end-to-end architecture and system design for large, complex ML Platform projects.
• Define technical direction and long-term platform strategy for ambiguous, high-impact problems.
• Design infrastructure for ML training, model serving, feature stores, real-time computation, monitoring, and LLM or agent orchestration.
• Lead projects from requirements and design through implementation and production operation.
• Balance latency, reliability, cost, security, and competing product priorities.
• Partner with ML engineers, data scientists, product teams, and senior leadership while mentoring engineers and improving MLOps maturity.

Requirements

• 10+ years of professional software development experience or equivalent domain expertise.
• Strong background in service-oriented architecture and large-scale distributed systems.
• Experience leading multi-team initiatives, providing technical direction, and mentoring engineers.
• Production experience building and operating ML platforms for model training, serving, orchestration, or ML data systems.
• Ability to work cross-functionally, communicate complex technical concepts, and operate with high autonomy in ambiguous environments.
• Hands-on experience using AI tools to accelerate software development.

Nice to have

• Experience with distributed ML training, accelerator-backed compute, training data pipelines, experiment tracking, model evaluation, feature stores, and model registries.
• Experience training and deploying machine learning models to production.
• Familiarity with LLMs, LLM application frameworks, agentic AI, retrieval-augmented generation, and multi-agent orchestration.
• Familiarity with AWS, SageMaker, Bedrock, Databricks, or OpenAI.
• Experience with prototypes, open source, side projects, or self-directed technical initiatives.

Culture & Benefits

• Flexible choice between office-based and remote work within the United States.
• Remote work is designed around regular home working with optional office attendance for meetings and events.
• Collaboration with geographically distributed engineering, data science, product, and infrastructure teams.
• Benefits may include equity, company bonuses, 401(k), medical, dental, vision, and wellness stipends.
• Work focused on increasing ML development velocity and powering ML-driven financial products at scale.