Senior ML Infrastructure Engineer (Agentic AI)

Candidate; Toronto; Remote; CAD 120,000 - 170,000; Full time

Benefits offered by this job

Lunch stipend

Health benefits

RRSP matching / Pension

Parental Leave

Learning stipend

Vacation 6 weeks

Travel to other offices

Home office stipend

Job summary

Cohere is hiring for a role in a security-first enterprise AI setting, focusing on scalable ML infrastructure and agentic systems. You will help build the platform that powers autonomous AI at scale, with focus on secure code execution, state management, and model routing.

This role suits candidates who thrive in hard, forward-looking problems and distributed environments. The team values systems depth, ML intuition, and rapid prototyping, with remote-friendly policies and global collaboration

Qualifications

• Experience building production ML infrastructure with strong systems fundamentals.
• Hands-on work with agentic systems, multi-agent workflows, or agent development frameworks.
• Familiarity with model routing and LLM provider frameworks across different model types and environments.

Responsibilities

• Turn emerging ML research ideas into production-ready infrastructure.
• Build core platform capabilities for execution, storage, and state management.
• Prototype and evaluate new technologies, then help decide what should move into production.
• Partner with research teams to shape infrastructure based on what future agent systems will need.

Skills

ML systems

Distributed systems

Kubernetes

Security

Production engineering

Tools

gVisor

Firecracker

Kata

WASM runtimes

Job description

Cohere is hiring for a role in a security-first enterprise AI setting, focusing on scalable ML infrastructure and agentic systems. You will help build the platform that powers autonomous AI at scale, with focus on secure code execution, state management, and model routing.

This role suits candidates who thrive in hard, forward-looking problems and distributed environments. The team values systems depth, ML intuition, and rapid prototyping, with remote-friendly policies and global collaboration