ML Engineering Manager, AI Conversation Platform & RAG

Stripe; Toronto; On-site; CAD 244,000 - 366,000; Full time

Benefits offered by this job

Equity

Performance bonus

Retirement plans

Health benefits

Wellness stipends

Job summary

Stripe is seeking an Engineering Manager for the AI Conversation Platform in Toronto. You will lead a growing ML-enabled team responsible for streaming feature pipelines, building ML models, and deploying them to production in a fast-paced, ambiguous environment.

You will set technical direction, mentor engineers, hire top talent, collaborate across product and partnerships, and prototype innovative integrations with customers.

Qualifications

• Have at least 4 years of experience managing ML teams
• Experience working as a Machine Learning Engineer, Applied Scientist or equivalent Individual Contributor.
• Lead by example in high‑growth, high‑impact, ambiguous environments
• Have experience building & shipping ML systems.
• Hold yourself and others to a high bar when working with production systems.
• Thrive in a collaborative cross‑functional environment
• Experience in shipping LLM & RAG systems

Responsibilities

• Driving an ambitious vision for AI/ML that benefits our users
• Setting the technical & process direction for the team based on business goals
• Brainstorm and coordinate product integrations with partner teams
• Proposing new ideas and building prototypes
• Be an integral part of a larger ML community internally & externally
• Hire & develop a world‑class team to deliver high‑quality ML systems.
• Coach engineers to help them grow in their careers and maintain a high bar

Skills

ML Leadership

Team management

Production ML systems

Streaming pipelines

Model deployment

Cross-functional collaboration

Hiring & mentoring

Prototyping

Ambiguity handling

Leadership

Tools

PyTorch

LLM tooling

Job description

Stripe is seeking an Engineering Manager for the AI Conversation Platform in Toronto. You will lead a growing ML-enabled team responsible for streaming feature pipelines, building ML models, and deploying them to production in a fast-paced, ambiguous environment.

You will set technical direction, mentor engineers, hire top talent, collaborate across product and partnerships, and prototype innovative integrations with customers.