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Reinforcement Learning Engineer Jobs in Reseda, CA

Spotter

Machine Learning Scientist

Culver City, CA · On-site

Collaborating closely with Product and Engineering to translate customer problems into machine learning solutions. * Staying current with advances in reinforcement learning, recommendation systems ...

Spotter

Machine Learning Scientist

Culver City, CA · Remote

Collaborating closely with Product and Engineering to translate customer problems into machine learning solutions. * Staying current with advances in reinforcement learning, recommendation systems ...

Paramount

Machine Learning Engineer

Burbank, CA · On-site

$109K - $143K/yr

... reinforcement learning. * High-Performance Inference: Design and maintain K8s-based inference ... Reliability Engineering: Define and enforce SLIs/SLOs for the platform, ensuring that ...

Paramount

Machine Learning Engineer

Burbank, CA · On-site

$109K - $143K/yr

... reinforcement learning. * High-Performance Inference: Design and maintain K8s-based inference ... Reliability Engineering: Define and enforce SLIs/SLOs for the platform, ensuring that ...

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All JobsReinforcement Learning Engineer JobsReinforcement Learning Engineer Jobs in Reseda, CA

Reinforcement Learning Engineer information

See Reseda, CA salary details

$40.8K

$124.5K

$205.8K

How much do reinforcement learning engineer jobs pay per year?

As of Aug 5, 2026, the average yearly pay for reinforcement learning engineer in Reseda, CA is $124,534.00, according to ZipRecruiter salary data. Most workers in this role earn between $89,200.00 and $162,800.00 per year, depending on experience, location, and employer.

What is a reinforcement learning engineer?

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 job categories do people searching Reinforcement Learning Engineer jobs in Reseda, CA look for? The top searched job categories for Reinforcement Learning Engineer jobs in Reseda, CA are:
What cities near Reseda, CA are hiring for Reinforcement Learning Engineer jobs? Cities near Reseda, CA with the most Reinforcement Learning Engineer job openings:
Reinforcement Learning Engineer jobs near you
Infographic showing various Reinforcement Learning Engineer job openings in Reseda, CA as of July 2026, with employment types broken down into 100% Full Time. Highlights an 75% In-person, and 25% Remote job distribution, with an average salary of $124,534 per year, or $59.9 per hour.

Senior Machine Learning Engineer

Absurd Ventures

Santa Monica, CA • On-site, Remote

$165K - $200K/yr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 20 days ago

Job description

We are seeking a Senior Machine Learning Engineer to help shape the future of content systems and real-time gameplay using cutting-edge machine learning techniques. As Senior Machine Learning Engineer, you will work alongside gameplay engineers, producers, designers, and artists to integrate intelligent, adaptive systems for our toolchain and in the realtime game. You will be a key player in evolving how our games are built and experienced. 

Core Responsibilities 

  • Work with team leads to develop and implement a machine learning (ML) strategy to maximize the potential to enhance our workflow and game systems.  Educate the team on what is possible with ML. 
  • Design and deploy LLM techniques, including agentic workflow and toolchain enhancements. 
  • Build pipelines that train on gameplay data and deploy models optimized for real-time inference.  Research and implement on-device small language and other local  models for scalable incorporation of ML features in the live game experience. 
  • Partner with design and gameplay teams to prototype and test ML-powered features such as smart NPC behaviors, simulation approximation and dynamic adaptive environments. 
  • Analyze gameplay telemetry to train models that enhance engagement, retention, and personalization. 
  • Develop internal tools using ML to streamline workflows for production, art, animation, or audio pipelines within Unreal Engine. 
  • Stay current with ML and real-time AI trends and ensure performance scalability across platforms. 
  • Mentor other engineers and promote best practices in integrating ML techniques. 

Core Qualifications 

  • 5+ years of machine learning experience, with at least 2 years integrating models into real-time or interactive environments. 
  • Strong C++ programming experience. 
  • Proficient in Python and ML libraries such as PyTorch or TensorFlow. 
  • Familiarity with training and deploying models for real-time inference with GPU/CPU constraints in mind. 
  • Understanding of supervised and unsupervised learning, reinforcement learning, and generative models. 
  • Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field. 

Plus If 

  • Experience with the Unreal Engine toolset (Blueprints, plugins, editor scripting, etc.). 
  • Experience building or using ML tools in a game development context.  
  • Knowledge of generative models (e.g. Diffusion, GANs, MCPs) for workflow augmentation. 

Description 

  • Full-time job with benefits. 
  • Role is on-site in Santa Monica, CA. 
  • Office is located near downtown Santa Monica - near metro and freeways. 

The base pay range for this position is $165,000 to $200,000 per year. Actual compensation is based on market location and may vary depending on job-related knowledge, skills, and experience. We also offer a competitive package of benefits including vacation time, sick time, company holidays, parental leave, medical/dental/vision insurance, life insurance, disability insurance, and 401(k) matching to regular full-time employees. Certain roles may also be eligible for bonus and equity. 

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