The opportunity
Stripe processes over $1. 9T in payments volume per year, which is roughly 1.
What you'll do
Take ownership of end-to-end architecture and system design for large, complex projects across ML Platform.
Define technical direction for highly ambiguous projects, transforming: complex user needs into long-lasting platform strategy.
Design system architectures for the most challenging ML Platform problems in: one or more areas, including AI and ML workflow orchestration, scalable CPU and GPU compute infrastructure, model training, LLM fine-tuning, low-latency model inference, large-scale feature stores, real-time monitoring, and LLM and 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.
What they're looking for
- + 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 building and operating production ML platform in one or more areas: such as model training, model serving, orchestration, or ML data systems, with requirements for performance, reliability, scalability, and cost efficiency.
- 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.
