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Reinforcement Learning Engineer Jobs in Reseda, CA

HoYoverse

Research Scientist

Los Angeles, CA · On-site

$180K - $300K/yr

... developing reinforcement learning tasks from end to end; project experience involving 3D environments, complex action space and spatial reasoning is a strong plus. * Proficiency in programming ...

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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 6, 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.

Software Engineer: On-board Autonomy

Lodestar

Los Angeles, CA • On-site

$140K - $180K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 14 days ago

Job description

About Lodestar
Lodestar's mission is to develop the first "Protect and Defend" capability for high-value space assets in orbit. Our flagship product, MITHRIL, is our hardware-agnostic, AI-enabled autonomy software suite that enables us to augment any off-the-shelf spacecraft with the ability to autonomously detect, characterise, and reversibly neutralise orbital threats. By building on the proven space heritage of our best-in-class satellite-bus partners and fully integrating MITHRIL into single unified platform, we deliver an end-to-end, autonomous in-space superiority service.
About the Job
At Lodestar, as a Software Engineer - On-board Autonomy, you'll be developing the decision-making systems at the heart of Lodestar's autonomy suite. You'll focus on creating algorithms that evaluate mission context, system capabilities, and environmental conditions to recommend and execute optimal actions. These models adapt dynamically to changing conditions, enabling real-time autonomous decision-making even in communications-limited or uncertain environments. Your work will bridge perception, prediction, and control, allowing spacecraft to operate intelligently, efficiently, and resiliently across complex mission scenarios.
We proudly have an "extreme ownership" oriented engineering culture.
What You'll Do
  • Design and implement on-board decision-making models that recommend and adapt strategies in real time
  • Develop autonomous decision algorithms that integrate information from perception, state estimation, and intent prediction models to execute mission objectives
  • Research and implement ML models for decision making - everything from lit. review, through training, to deployment
  • Implement decision models that adapt dynamically to changing mission context, environmental conditions, and system status
  • Develop frameworks for continuous re-evaluation of active strategies to ensure resilient and adaptive behavior under uncertainty
  • Support real-time autonomy in communications-limited or time-critical scenarios
  • Build and maintain autonomy infrastructure, testing frameworks, and deployment pipelines for space missions
Basic Qualifications
  • Bachelor's or Master's degree in Computer Science, Machine Learning, Robotics, a related field, or equivalent experience
  • 2+ years of distinguished industry experience in autonomy, decision-making, or control systems for aerospace/robotics
  • Strong proficiency in C++ and Python and DL frameworks (PyTorch, TensorFlow)
  • Demonstrated experience with machine learning applied to decision-making or control problems
  • Track record with optimal control, planning, or reinforcement learning in real-time systems
  • Familiarity with multi-agent decision-making or planning under uncertainty
Preferred Skills & Experience
  • Track record implementing autonomy applications in real-time or safety-critical environments
  • Experience integrating perception and prediction outputs into decision frameworks
  • Familiarity with resource-aware strategy selection and optimization under uncertainty
  • Background in reinforcement learning, hierarchical planning, or adaptive control
  • Experience with distributed training and cloud-based scaling of ML models (AWS, GCP, or Azure)
  • Experience with Linux, Git, and CI/CD pipelines
  • Comfortable with containerization tools such as Docker and Kubernetes
  • Familiarity with real-time systems, multi-threading, and performance optimization
  • Strong understanding of distributed autonomy, networking, and communication protocols
ITAR Requirements
To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here.
Compensation & Benefits
  • Pay Range:
    • (E1) Junior Software Engineer: $120,000 - $145,000 / year
    • (E2) Software Engineer: $140,000 - $180,000 / year
    • (E3) Senior Software Engineer: Competitive
  • Meaningful equity incentives as part of our employee option pool
  • Flexible PTO with generous paid vacation, holidays, and sick leave
  • Comprehensive medical, dental & vision coverage
  • 401(k) retirement plan with company match
  • LA
Additional Information
Compensation bands are determined by role, level, location, and alignment with market data. Individual level and base pay is determined on a case-by-case basis and may vary based on job-related skills, education, experience, technical capabilities and internal equity. In addition to base salary, for full-time hires, you may also be eligible for long-term incentives, in the form of stock options , and access to medical, vision and dental coverage, as well as access to a 401(k) retirement plan.
Lodestar is an Equal Opportunity Employer; employment with Lodestar is governed on the basis of merit, competence and qualifications and will not be influenced in any manner by race, color, religion, gender, national origin/ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability or any other legally protected status.

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