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Reinforcement Learning Engineer Jobs in California

As a Machine Learning Engineer on our core AI/ML team, you will design and build GenAI-powered ... and reinforcement learning Solid understanding of core machine learning concepts, including ...

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a ... Experience with interactive machine learning (eg. active learning, reinforcement learning, machine ...

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a ... Experience with interactive machine learning (eg. active learning, reinforcement learning, machine ...

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Reinforcement Learning Engineer information

See California salary details

$37.5K

$114.3K

$189K

How much do reinforcement learning engineer jobs pay per year?

As of Jun 15, 2026, the average yearly pay for reinforcement learning engineer in California is $114,347.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,900.00 and $149,500.00 per year, depending on experience, location, and employer.

What are Reinforcement Learning Engineers?

Reinforcement Learning Engineers are specialized professionals who design, develop, and implement algorithms based on reinforcement learning, a type of machine learning where agents learn to make decisions by receiving rewards or penalties. They work on building models that enable machines to learn optimal actions through trial and error in complex environments. Their responsibilities often include developing RL architectures, tuning hyperparameters, running simulations, and applying RL methods to real-world problems like robotics, gaming, or recommendation systems. RL Engineers typically have strong backgrounds in computer science, mathematics, and deep learning, along with experience in programming languages like Python and frameworks such as TensorFlow or PyTorch.

What are the key skills and qualifications needed to thrive as a Reinforcement Learning Engineer, and why are they important?

To thrive as a Reinforcement Learning Engineer, you need a strong background in machine learning, mathematics (especially probability and statistics), and programming languages like Python, often supported by a relevant degree in computer science or engineering. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), RL libraries (like OpenAI Gym), and cloud computing platforms is typically required. Problem-solving skills, creativity, and effective collaboration help set outstanding engineers apart in this field. These competencies enable the design and deployment of advanced RL solutions that address real-world challenges and drive innovation.

What are some common challenges faced by Reinforcement Learning Engineers when deploying models in real-world environments?

One of the main challenges Reinforcement Learning (RL) Engineers face is bridging the gap between simulation and real-world deployment. Models that perform well in controlled environments may struggle with unpredictable data, safety constraints, or limited feedback in production. Additionally, RL algorithms often require significant computational resources and careful tuning to avoid instability. Collaboration with domain experts and software engineers is essential to address these issues and ensure successful integration of RL solutions into existing systems.

What is the difference between Reinforcement Learning Engineer vs Machine Learning Engineer?

AspectReinforcement Learning EngineerMachine Learning Engineer
CredentialsBachelor's/Master's in CS, AI, or related; experience with RL frameworksBachelor's/Master's in CS, Data Science, or related; experience with ML algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on RL applicationsTech companies, data-driven firms, AI departments across industries
Industry UsageSpecialized in RL projects like robotics, game AI, autonomous systemsBroader applications including predictive modeling, NLP, computer vision

Reinforcement Learning Engineers focus on developing algorithms that learn through interactions with environments, often in robotics or gaming. Machine Learning Engineers work on a wider range of models and applications. While both roles require strong programming and math skills, RL Engineers specialize in sequential decision-making, whereas ML Engineers handle diverse data-driven tasks across industries.

What cities in California are hiring for Reinforcement Learning Engineer jobs? Cities in California with the most Reinforcement Learning Engineer job openings:
Infographic showing various Reinforcement Learning Engineer job openings in California as of June 2026, with employment types broken down into 100% Full Time. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $114,347 per year, or $55 per hour.
Principal Machine Learning Engineer, Embodied AI and Smart NPCs

Principal Machine Learning Engineer, Embodied AI and Smart NPCs

Roblox

San Mateo, CA โ€ข On-site

Other

Posted 25 days ago


Job description

As a Principal Machine Learning Engineer within the Creator Services Machine Intelligence team, you will focus on the research and development of Embodied AI and Behavioral Agents that revolutionize how games are created and played on Roblox. You will bridge the gap between cutting-edge research and massive-scale product application, building agents capable of complex 3D gameplay and unblocking many use cases across Roblox, from automated playtesting to ensure quality, to "Smart NPCs" with human-like movement and strategic reasoning, playing with real players in games.

You will work on Imitation Learning (IL), Reinforcement Learning (RL), Computer Vision, Computer Graphics, Robotics, and Agentic Reasoning to create generalizable agents that can perceive 3D environments, understand game rules, plan long-term strategies, and execute complex physics-based actions.

You Will:
  • Report to the Engineering Manager of Creator Services Machine Intelligence.
  • Design and implement foundation models for embodied agents that master both fluid, human-like movement and high-level strategic reasoning.
  • Define the long-term roadmap for Game AI and Embodied Intelligence, acting as a technical bar-raiser for code quality and architectural design.
  • Balance the exploration of cutting-edge deep learning research with the practical constraints of serving models to millions of concurrent users.
  • Mentor fellow engineers and researchers, fostering a culture of technical excellence and scientific inquiry.
  • Collaborate with Product Managers, Backend and Game Engine Engineers and other Roblox team members.
You Have:
  • PhD or Master's in Computer Science, Applied Math, or other field. A record of top-tier publications (e.g., NeurIPS, ICML, CVPR, AAAI, SIGGRAPH, etc) in embodied agents or related domains is a plus.
  • 7+ years of experience as a Machine Learning Engineer or Research Scientist, applying research to tangible products.
  • Deep technical understanding of Imitation Learning, Robotics, Reinforcement Learning, Computer Graphics and Vision, with experience working on low-latency motor control (human-like movement) and high-level strategic reasoning (game rules/goals).
  • Proficiency in Python (PyTorch/TensorFlow) and familiarity with C#, C++, or similar systems languages.
  • Experience training models on large-scale distributed clusters and understanding the challenges of inference in real-time gaming environments.
  • A passion for bridging the gap between research and production, moving beyond academic benchmarks to launch scalable solutions that directly impact millions of users.