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Reinforcement Learning Engineer - Ingénieur(e) en apprentissage par renforcement

NBCUniversal
Full Timemid
Montréal, QC, CanadaPosted 10 days ago

Role Overview

NBCUniversal is hiring a mid-level Reinforcement Learning Engineer - Ingénieur(e) en apprentissage par renforcement. This is a full-time role in Montréal. posted last week. Full responsibilities, required qualifications, and the apply link are listed in the description below.

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ReinforcementLearningEngineerKeyRespons

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 Respons...

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Frequently Asked Questions

How do I apply for the Reinforcement Learning Engineer - Ingénieur(e) en apprentissage par renforcement position at NBCUniversal?

Use the Apply button above to submit your application directly to NBCUniversal. Most applications take less than 5 minutes if your resume and contact details are ready, and you'll be routed to the employer's official application system to finish.

Where is the Reinforcement Learning Engineer - Ingénieur(e) en apprentissage par renforcement position at NBCUniversal located?

This position is based in Montréal. NBCUniversal has not indicated remote or hybrid options for this role, so candidates should plan for on-site work.

What does a Reinforcement Learning Engineer - Ingénieur(e) en apprentissage par renforcement at NBCUniversal earn?

NBCUniversal has not disclosed a salary range in this posting. Many employers share specifics later in the interview process; you can also ask during a recruiter screen if compensation transparency is important to you.

When was the Reinforcement Learning Engineer - Ingénieur(e) en apprentissage par renforcement role at NBCUniversal posted?

This role was posted on July 6, 2026 (10 days ago). It's still listed as actively hiring; we re-confirm openings against the source system multiple times per day and remove closed roles.

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