Staff Software Engineer, Machine Learning Platform

Stripe logo Jobs Our opportunity Life at Stripe Benefits University See open roles Open mobile navigation Jobs Our opportunity Life at Stripe Benefits University See open roles Close mobile navigation Roles at Stripe Role details Staff Software Engineer, Machine Learning Platform Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team Stripe processes over $1.9 T in payments volume per year, which is roughly 1.6% of the world's GDP, for millions of customers from startups to enterprises. The tremendous amount of data makes Stripe one of the best places to do machine learning. While being an integral part of almost every product line at Stripe (e.g., Payments, Radar, Capital, Billing, etc.), we have lots of exciting opportunities to innovate in ML Platform at Stripe. The ML Platform team builds the platforms and services that enable ML engineers and data scientists across Stripe to take the data and build features and models from prototype to production — reliably, at low latency, and at scale. We work closely with product teams, data scientists, and platform infrastructure teams to build powerful, flexible, and user-friendly systems that substantially increase ML velocity across the company. What you'll do You will serve as a technical lead across the ML Platform space and a key contributor to the evolution of the platforms that power Stripe's ML-driven products. As a Staff Engineer, you'll be empowered to make decisions with a large impact on Stripe. You will influence our investments and strategy while making our systems more reliable, secure, and a delight to use. You will work cross-functionally with other tech staff, data science, product, and senior leadership to land a bigger impact of ML at Stripe. You will help define the long-term strategy and lead the technical direction for the next generation of ML infrastructure that powers 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. Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum 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. In-office expectations Office-assigned Stripes in most of our locations are currently expected to spend at least 50% of the time in a given month in their local office or with users. This expectation may vary depending on role, team and location. For example, Stripes in Stripe Delivery Center roles in Mexico City, Mexico, Bengaluru, India, and Dublin, Ireland work 100% from the office. Also, some teams have greater in-office attendance requirements, to appropriately support our users and workflows, which the hiring manager will discuss. This approach helps strike a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility when possible. Pay and benefits The annual US base salary range for this role is $224,000 - $336,000. For sales roles, the range provided is the role's On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. This salary range may be inclusive of several career levels at Stripe and will be narrowed during the interview process based on a number of factors, including the candidate's experience, qualifications, and location. Applicants interested in this role and who are not located in the US may request the annual salary range for their location during the interview process. Additional benefits for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends. Office locations South San Francisco HQ, or Seattle Team Machine Learning Job type Full time Apply for this role Please find our California applicant personal information notice here . We look forward to hearing from you At Stripe, we're looking for people with passion, grit, and integrity. You're encouraged to apply even if your experience doesn't precisely match the job description. Your skills and passion will stand out—and set you apart—especially if your career has taken some extraordinary twists and turns. At Stripe, we welcome diverse perspectives and people who think rigorously and aren't afraid to challenge assumptions. Join us. 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