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Deep Reinforcement Learning Jobs in California (NOW HIRING)

Skild AI

Machine Learning Engineer

San Mateo, CA

Deep understanding and practical experience with various reinforcement learning algorithms and techniques (model-free, model-based, multi-task, hierarchical, multi-agent, etc.). * Strong background ...

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Deep Reinforcement Learning information

What is deep reinforcement learning?

A Deep Reinforcement Learning (DRL) job involves researching, developing, and applying AI models that use reinforcement learning techniques combined with deep learning. Professionals in this role design algorithms that enable agents to learn optimal decision-making policies through trial and error. Common applications include robotics, game AI, autonomous systems, and financial modeling. This job typically requires expertise in machine learning, neural networks, and programming languages like Python, along with frameworks such as TensorFlow or PyTorch.

What does a typical day look like for someone working in deep reinforcement learning?

A typical day for a Deep Reinforcement Learning professional involves designing algorithms, running experiments, analyzing results, and optimizing models to improve performance. You may collaborate regularly with data scientists, software engineers, and domain experts to integrate RL solutions into larger systems or products. Tasks often include reading the latest research, contributing to code reviews, and documenting findings while troubleshooting technical challenges. This dynamic environment encourages continuous learning and teamwork, ensuring you stay at the forefront of AI innovation.

What are the key skills and qualifications needed to thrive in deep reinforcement learning?

To thrive in Deep Reinforcement Learning, you need expertise in machine learning, programming (Python, TensorFlow, or PyTorch), and applied mathematics, often supported by an advanced degree in computer science or a related field. Familiarity with version control systems, cloud computing platforms, and relevant certifications in AI or data science are valuable assets. Strong problem-solving abilities, collaboration, and effective communication are important soft skills in this position. These skills are essential for developing, implementing, and iterating cutting-edge algorithms that solve complex real-world problems in dynamic environments.

What are the most commonly searched types of Deep Reinforcement Learning jobs in California?

The most popular types of Deep Reinforcement Learning jobs in California are:

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For Deep Reinforcement Learning jobs in California, the most frequently searched job titles are:

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Deep Reinforcement Learning jobs near you
Infographic showing various Deep Reinforcement Learning job openings in California as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Research Scientist, Reinforcement Learning

Deeproute.ai

Fremont, CA • On-site

Full-time

Re-posted 10 days ago

Job description

We are building next-generation end-to-end autonomous driving systems powered by reinforcement learning.
You will work on applying RL in closed-loop, safety-critical environments, leveraging large-scale simulation and real-world driving data to improve safety, comfort, and robustness.
  • Train and deploy RL policies in closed-loop driving environments
  • Scale RL training using massively parallel simulation systems
  • Design and optimize reward functions for complex driving behaviors
  • Improve sim-to-real transfer for real-world robustness
  • Collaborate with cross-functional teams to integrate models into production systems

Requirements
Core Technical Skills
  • Proficiency in modern RL algorithms: DQN, PPO, SAC, TD3, etc.
  • Proficiency in modern RLHF algorithms: PPO, DPO, GRPO, etc.
  • Hands-on experience training reward models and finetuning LLM/VLM/VLA
  • Knowledge of distributed RL training at scale
  • Proficiency with massively parallel simulation environments
  • Knowledge of sim-to-real transfer techniques and domain randomization
  • Proficiency in Python, comfortable with C++
  • Proficiency in deep learning frameworks such as PyTorch
  • Experience with distributed training frameworks (Ray, Horovod, etc.)
  • Knowledge of model optimization (quantization, pruning) and CUDA is a plus
  • Knowledge of traffic rules, driving behavior modeling

Preferred Qualifications
  • Publications in top-tier venues (ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV, ICRA, IROS, etc.)
  • Open-source contributions to RL libraries or autonomous driving projects
  • Previous experience with LLM fine-tuning using RLHF
  • Knowledge of safe RL, interpretable AI, or robustness techniques
  • Familiarity with autonomous vehicle regulations and safety standards

Apply