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

NBC Universal
Montreal
Workplace: HybridFull timeFunction: Solutions Engineering & Sales EngineeringEducation: mastersSkills: ["Cross-functional collaboration","Strong mathematical background","Debugging","Attention to detail","Precision","Ability to analyze and adapt"]

Design and build high-fidelity 2D/3D simulation environments to train reinforcement learning agents for complex, multi-sensor scenarios. You’ll collaborate with ML, annotation, and TPM partners to define data and training requirements; engineer reward functions aligned to product goals and safety constraints; implement and optimize RL algorithms (e.g., PPO, SAC, Offline RL); and improve sim-to-real performance via domain randomization/adaptation.

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FursaFursa
NBC Universal
NBC Universal
18 hours ago

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

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Last checked: 4 hours agoStatus: Live

Job Summary

Design and build high-fidelity 2D/3D simulation environments to train reinforcement learning agents for complex, multi-sensor scenarios. You’ll collaborate with ML, annotation, and TPM partners to define data and training requirements; engineer reward functions aligned to product goals and safety constraints; implement and optimize RL algorithms (e.g., PPO, SAC, Offline RL); and improve sim-to-real performance via domain randomization/adaptation.
Location: Montreal
Workplace: Hybrid
Employment Type: Full time
Job Function: Solutions Engineering & Sales Engineering
Seniority: Mid level

Key Responsibilities

  • •Coordinate with partner ML and Annotation engineers and TPMs to define data, simulation, and training requirements.
  • •Build and maintain high-fidelity 2D/3D simulation environments using tools such as Unity, Unreal, or Isaac Sim for RL agent training.
  • •Design and tune reward functions to align agent behavior with product goals and safety constraints.
  • •Develop and optimize RL algorithms (e.g., PPO, SAC, Offline RL) for high-dimensional 3D observation spaces.
  • •Address sim-to-real performance by analyzing the reality gap and implementing domain randomization or adaptation techniques.

Key Requirements

  • •Graduate degree (Master’s or PhD) in Robotics, Computer Science, AI, or related field focused on reinforcement learning, imitation learning, or online machine learning.
  • •Proven experience as an RL Engineer or Research Engineer in a fast-paced environment.
  • •Experience in industries with complex multi-disciplinary teams such as robotics, smart grids, precision agriculture, game development, or aerospace.
  • •Fluency with Python, Git, and Unix shell; strong mathematical background for MDPs and gradient-based optimization.
  • •Deep familiarity with RL frameworks such as Ray RLlib, Stable Baselines3, or CleanRL; experience with physics engines or 3D game engines.
  • •High attention to detail for debugging non-deterministic agent behaviors and ensuring environment parity.
Experience:RoboticsSmart gridsPrecision agricultureGame developmentAerospaceFast-paced environment
Education:Master's in Robotics, Computer Science, AI (or related field)
Skills:Cross-functional collaborationStrong mathematical backgroundDebuggingAttention to detailPrecisionAbility to analyze and adapt
Languages:English
Tech Stack:PythonGitUnix shellRay RllibStable Baselines3CleanRLUnityUnrealIsaac SimMuJoCoBulletJiraConfluenceSlackPPOSACOffline RLDomain randomizationAdaptation

Company Brief

NBC Universal
NBCUniversal is a global media and entertainment company producing and distributing film, television, news, sports and streaming content, and operating theme parks and consumer experiences across a portfolio of well-known brands including NBC, Universal Pictures and Peacock.
Industry: Film & Television
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Established Company
Headquarters: New York City, United States
Founded: 2004
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