Staff Machine Learning Engineer, ML Foundations (Technical Leader)
San Francisco, CA or Seattle, Wa
Full Time Senior-level / Expert USD 254K - 538K
Stripe
Stripe powers online and in-person payment processing and financial solutions for businesses of all sizes. Accept payments, send payouts, and automate financial processes with a suite of APIs and no-code tools.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
Machine learning is an integral part of almost every service at Stripe. It is a key investment area with products and use cases that span merchant and transaction risk, payments optimization, identity, and merchant data analytics and insights (Sigma). We are also using the latest generative AI technologies (such as LLMs and FMs) to re-imagine product experiences and developing AI Assistants both for our customers (e.g. Radar Assistant and Sigma Assistant), and also to make Stripes more productive across Support, Marketing, Sales, and Engineering roles within the company.
From a data perspective, Stripe handles over $1T in payments volume per year, which is roughly 1% of the world’s GDP. We process petabytes of financial data using our ML platform to build features, train models, and deploy them to production. We use a combination of highly scalable and explainable models such as linear/logistic regression and random forests, along with the latest deep neural networks from transformers to LLMs. Some of our latest innovations have been around figuring out how best to bring transformers and LLMs to improve existing models and also enable entirely new product ideas that are only made possible by GenAI.
What you’ll do
As part of the ML Foundations organization you will play a critical role in the acceleration of our Machine Learning journey at Stripe. The organization develops the AI/ML foundational platform features and GenAI models to enable all Stripes to create AI/ML powered product features and applications. As a lead you will be responsible for helping build out the Machine Learning roadmap for the organization, end to end model development, and driving Machine Learning and GenAI initiatives. You will also coach and mentor our engineering talent, and work closely with engineering leadership and large cross-functional teams including engineering, data scientists and product teams to help scale the AI/ML efforts.
Responsibilities
- Develop and execute against both short and long-term roadmaps. Make effective tradeoffs that consider business priorities, user experience, and a sustainable technical foundation
- Design, implement, and scale critical machine learning model development to support company wide strategic initiatives
- Improve existing models to help enable new product ideas and improve productivity for our users and across the company
- Assist with team growth and development while maintaining a high bar for excellence and technical curiosity
- Own and build cross-functional partnerships with stakeholders including dependency engineering teams, product, design, infrastructure, and operations
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
- Minimum of 15+ years of ML engineering industry experience OR equivalent combined work experience reflecting domain expertise as relevant to this position
- PhD in a relevant field (computer science, machine learning, AI, statistics, physics, …)
- Strong understanding of machine learning approaches and algorithms: Deep Learning, LLM, Generative Models, NLP
- Demonstrated experience of leading company-wide initiatives spanning multiple teams and organizations OR leveraging deep domain expertise to influence tech roadmap planning and execution
- Demonstrated ability to effectively collaborate across multiple teams and stakeholders to drive business outcomes
- Experience, mentoring, and investing in the development engineers and peers
Hybrid work at Stripe
Office-assigned Stripes spend at least 50% of the time in a given month in their local office or with users. This hits a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility about how to do this in a way that makes sense for individuals and their teams.Pay and benefits
The annual US base salary range for this role is $254,600 - $538,400. 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.
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.Tags: Computer Science Data Analytics Deep Learning Engineering Generative AI Generative modeling LLMs Machine Learning ML models NLP PhD Physics Radar Statistics Transformers
Perks/benefits: 401(k) matching Career development Equity Health care Salary bonus Startup environment Wellness
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