Engineering Manager, Machine Learning Platform

Location: Toronto

Department: 8212 ML Foundations

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 have the opportunity to shape the future of ML Data Foundations platform, ML Serving and observability at Stripe. You will help define the long-term strategy and lead the team in building the next generation of serving infrastructure that powers most if not all of Stripe's ML-driven products.

Responsibilities

• Hire, lead and manage a team of talented engineers on the team, providing mentorship, guidance, and support to ensure their success.
• Collaborate with cross-functional teams, including senior leadership, ML Platform teams, data platform, data science, machine learning, and other business orgs to understand user needs and translate them into technical solutions.
• Define the vision and roadmap aligning it with business objectives and industry best practices.
• Drive the execution of projects, overseeing the entire development lifecycle from planning to delivery, while maintaining high standards of quality and timely completion.
• Foster a collaborative and inclusive work environment, promoting innovation, knowledge sharing, and continuous improvement within the team.
• Stay up-to-date with emerging technologies, industry trends, and advancements in AI/ML to identify opportunities for innovation and improvement.
• Communicate effectively with stakeholders, providing regular updates on project status, progress, and any potential risks or challenges.

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 software development experience and 3+ years of engineering management experience building user-centric products
• Proven track record of building and operating large scale, highly available, low-latency systems
• Experience working in highly cross-functional organizations
• The ability to thrive on a high level of autonomy and responsibility
• The desire to encourage a healthy, inclusive work environment that's both supportive and challenging
• Clear and persuasive writing and in-person communication

Preferred qualifications

• Experience in building large-scale serving or data infrastructure for machine learning use cases (e.g., model inference, feature stores, real-time feature computation)
• Familiarity with cloud services (e.g., AWS) and cloud-based AI/ML services (e.g., SageMaker, Bedrock, Databricks, OpenAI, etc.)
• Comfortable working with geographically distributed teams
• Experience with applying machine learning to real-world problems