Senior RL Engineer - Ingénieur(e) principal(e) en apprentissage par renforcement

Montréal, Québec

On-site

Full-time

No salary posted1 weeks ago

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Employment type: Full-time · On-site
Posted: September 24, 2026
Province: Quebec (QC)

Hiring confidence: Sparse posting · 11/100

This posting is missing most of the signals we look for, or shows patterns common to listings that are not actively being filled. Check with the employer before applying. How this is scored

Job Overview

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Job Description We are seeking a Reinforcement Learning Engineer with experience manipulating virtual environments to train autonomous agents. This role focuses on the design of robust simulation environments, reward structures, and policy architectures that can navigate complex, multi-sensor landscapes. Key Responsibilities Cross-Functional Coordination: Work with partner ML and Annotation engineers and TPMs to spec out data, simulation, and training requirements. Environment Design: Build and…

This role is based in Quebec. NBCUniversal is hiring for this full-time position through Jobily, where you can check how well your resume matches the posting before you apply.

Market context

About this role in Quebec

Average salary for Senior RL Engineer - Ingénieur(e) principal(e) en apprentissage par renforcement in Quebec

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NOC code

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Typical qualifications for Canadian employers

Experience manipulating virtual environments
Experience training autonomous agents
Knowledge of simulation environment design
Understanding of reward structures
Experience with policy architectures

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What you'll do

Design robust simulation environments
Design reward structures for training
Design policy architectures
Coordinate across ML and Annotation engineering teams
Work with Technical Program Managers to specify data requirements
Work with Technical Program Managers to specify simulation requirements
Work with Technical Program Managers to specify training requirements
Build virtual environments for autonomous agent training

What you'll need

Experience manipulating virtual environments
Experience training autonomous agents
Knowledge of simulation environment design
Understanding of reward structures
Experience with policy architectures
Ability to work with complex, multi-sensor systems