Stripe02.06.2026
Staff Software Engineer, Machine Learning Platform
Зарплата не указана
San Francisco
Обязанности
- 01Take ownership of end-to-end architecture and system design for large, complex projects across ML Platform
- 02Define technical direction for highly ambiguous projects, transforming complex user needs into long-lasting platform strategy
- 03Design 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
- 04Turn high-leverage ideas into tangible, robust solutions that shape platform and product roadmap, combining technical excellence with creative problem-solving
- 05Scope and lead large projects with significant business impact, driving them from requirements through design, implementation, and production operation
- 06Work with ML engineers, data scientists, and product teams directly to translate their needs into functional requirements and scalable technical solutions
- 07Arbitrate critical decisions that balance competing priorities while meeting latency, reliability, cost, and security constraints
- 08Serve 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
- 09Drive cross-team technical initiatives that improve ML development velocity and MLOps maturity across the company
- 10Mentor and grow other engineers
- 11Serve as a role model for designing, implementing, and operating great software systems
Требования
- 0110+ years of professional software development experience, or equivalent domain expertise, with a solid background in service-oriented architecture and large-scale distributed systems
- 02Track record of serving as a technical lead, with the ability to provide technical direction, lead multi-team initiatives, and mentor team members
- 03Experience 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
- 04Strong product instincts and a deep understanding of the business context in which you operate
- 05Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders
- 06Demonstrated ability to work cross-functionally, collaborating effectively with ML engineers, data scientists, software engineers, product managers, and business stakeholders
- 07The ability to thrive on a high level of autonomy and responsibility, and comfort operating in ambiguous environments
- 08Hands-on experience using AI tools to accelerate how you work
- 09Experience building large-scale ML training, serving, or data infrastructure for machine learning use cases, such as distributed training, model inference, feature stores, real-time feature computation, and model registries
- 10Experience with distributed ML training systems, accelerator-backed compute, training data pipelines, experiment tracking, and model evaluation
- 11Experience rapidly developing prototypes and iterating based on user feedback
- 12Experience training and shipping machine learning models to production to solve critical business problems
- 13Familiarity with LLMs, LLM application frameworks, and agentic AI patterns (e.g., tool use, multi-agent orchestration, retrieval-augmented generation)