Stripe is hiring a Machine Learning Engineer in Toronto!
Stripe's Applied ML team in Toronto is hiring a Machine Learning Engineer to reshape how businesses interact with Stripe's financial infrastructure. The group works end-to-end — from framing an opportunity through modeling, experimentation, and shipping to production — and combines modern LLM integrations with models the team fine-tunes itself. Example problem areas include helping customers resolve issues more quickly and smoothing sign-up and onboarding flows. This is a hands-on role for someone who enjoys moving fluidly between analysis, model training, evaluation, and production engineering, and who wants a voice in how ML architecture evolves across Stripe more broadly.
Key responsibilities
- Investigate product opportunities, propose ML approaches, and carry ideas through to production deployment
- Build pipelines and automated processes for training and evaluating models both offline and online
- Integrate models into production systems with attention to scalability and reliability
- Design evaluation strategies that catch quality regressions once models are live
- Test whether fine-tuning LLMs outperforms alternative approaches for a given problem
- Partner with product and strategy stakeholders to scope, prioritize, and launch new features
- Contribute to decisions about which open-source and in-house ML platforms to invest in
- Keep current with ML/AI research and take calculated risks turning new ideas into shipped solutions
Key requirements
- At least 3 years of experience shipping machine learning systems to production
- Experience building streaming feature pipelines and deploying models, including backend changes where needed
- A high bar for production quality and a strong sense of ownership over business outcomes
- Comfort with ambiguity, initiative, and a bias toward action in collaborative teams
- Preferred: 5 or more years in full-time software development roles
- Preferred: track record shipping LLM integrations into user-facing products at high quality
- Preferred: experience deriving hypotheses from data and operating in highly ambiguous settings
- Seniority: Mid-level (3+ years shipping ML systems; 5+ years software development preferred)
- Work mode: Not specified
- Key skills: Machine learning, Large language models (LLMs), Fine-tuning, Streaming feature pipelines, Model evaluation (offline and online), Production deployment, Experimentation, Cross-functional collaboration
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