AI role · Open offer

ML Engineer — OpenAI ecosystem (Toronto)

OpenAI

Full-time
Senior
Applied ML
Hybrid
Toronto, ON, Canada · Hybrid
Toronto, Canada
CAD 160k–220k/year + equity

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

4 skills2 tools

At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: Toronto, ON, Canada · Hybrid
Compensation: CAD 160k–220k/year + equity
Experience level: Senior
Team: Applied ML
Benefits & perks

About this role

What the team is looking for and how this position fits.

OpenAI is hiring a Senior ML Engineer — OpenAI ecosystem (Toronto) on the Applied ML team (Hybrid · Toronto, Canada). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

OpenAI product and applied ML teams hire across North America — including OpenAI jobs Toronto intent.

ML Engineer (hybrid Toronto): GPT/API product ML, evals, and safety loops for Canadian-facing delivery.

Missions

Own eval suites tied to user outcomes.
Ship applied ML features with latency and cost budgets.

Stack & environment

Python, PyTorch, Docker, Kubernetes.

What we look for

LLM/ML product experience; Canada work authorization.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
NLP

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: GPT/API product ML or eval systems with clear ownership.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, NLP, Machine Learning, and Kubernetes.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Toronto, Canada)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: CAD 160k–220k/year + equity, benefits in At a glance) and your expectations align.

Apply

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What the employer asked for

GPT/API product ML or eval systems with clear ownership.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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