Staff Software Engineer, Machine Learning Platform

About the role

You will serve as a technical lead across the Machine Learning Platform space and a key contributor to the evolution of the platforms that power Stripe's ML-driven products.

Responsibilities

• Take ownership of end-to-end architecture and system design for large, complex projects across ML Platform.
• Define technical directions for projects with high ambiguity, transforming complex user needs into long-lasting platform strategy.
• Design the system architecture and solutions for the most challenging problems in the ML Platform domain, including low-latency model inference, large-scale feature stores, real-time monitoring, and LLM/agent orchestration.
• Turn high-leverage ideas into tangible, robust solutions that shape platform and product roadmap, combining technical excellence with creative problem-solving.
• Scope and lead large projects with significant business impact, driving them from requirements through design, implementation, and production operation.
• Work with ML engineers, data scientists, and product teams directly to translate their needs into functional requirements and scalable technical solutions.
• Arbitrate critical decisions that balance competing priorities while meeting latency, reliability, cost, and security constraints.
• Serve as a key engineering representative, engaging senior leaders across Stripe and advising the leadership team on key technical considerations related to the end-to-end ML lifecycle.
• Drive cross-team technical initiatives that improve ML development velocity and MLOps maturity across the company.
• Mentor and grow other engineers. Serve as a role model for designing, implementing, and operating great software systems.

Requirements

• 10+ years of professional software development experience, or equivalent domain expertise, with a solid background in service-oriented architecture and large-scale distributed systems.
• Track record of serving as a technical lead, with the ability to provide technical direction, lead multi-team initiatives, and mentor team members.
• Experience working on production ML platform services.
• Strong product instincts and a deep understanding of the business context in which you operate.
• Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders.
• Demonstrated ability to work cross-functionally, collaborating effectively with ML engineers, data scientists, software engineers, product managers, and business stakeholders.
• The ability to thrive on a high level of autonomy and responsibility, and comfort operating in ambiguous environments.
• Hands-on experience using AI tools to accelerate how you work.

Preferred qualifications

• Experience building large-scale serving or data infrastructure for machine learning use cases (e.g., model inference, feature stores, real-time feature computation, model registries).
• Familiarity with LLMs, LLM frameworks, and agentic AI patterns (e.g., tool use, multi-agent orchestration, retrieval-augmented generation).
• Experience rapidly developing prototypes and iterating based on user feedback.
• Familiarity with cloud services (e.g., AWS) and cloud-based AI/ML services (e.g., SageMaker, Bedrock, Databricks, OpenAI).
• Experience training and shipping machine learning models to production to solve critical business problems.
• Ability to synthesize ideas across the organization while setting a compelling technical vision.
• Comfortable working with geographically distributed teams.
• Passion for side-projects, open source, or self-driven technical initiatives.