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Deep Reinforcement Learning Jobs in California (NOW HIRING)

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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:

What are popular job titles related to Deep Reinforcement Learning jobs in California?

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

What job categories do people searching Deep Reinforcement Learning jobs in California look for?

The top searched job categories for Deep Reinforcement Learning jobs in California are:

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

Institute of Foundation Models

Sunnyvale, CA • On-site

$150K - $450K/yr

Full-time

Re-posted 11 days ago

Job description

About the Institute of Foundation Models
We are a dedicated research lab for building, understanding, using, and risk-managing foundation models. Our mandate is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy.
As part of our team, you'll have the opportunity to work on the core of cutting-edge foundation model training, alongside world-class researchers, data scientists, and engineers, tackling the most fundamental and impactful challenges in AI development. You will participate in the development of groundbreaking AI solutions that have the potential to reshape entire industries.Strategic and innovative problem-solving skills will be instrumental in establishing MBZUAI as a global hub forhigh-performance computing in deep learning, driving impactful discoveries that inspire the next generation of AIpioneers.
Position Summary
As a Research Scientist within our Reinforcement Learning team, you will play a fundamental role in establishing our scientific and technical directions toward the development of emergent capabilities within Foundation Models. The role involves pioneering novel approaches within Reinforcement Learning to facilitate paradigm shifts in foundation modeling. The role involves prototyping and adapting novel approaches to learning from experience, contributing to large-scale RL training infrastructure, and produce replicable code for public release. You will also be expected to build and maintain a productive research portfolio, supported by internal and external collaborations.
Key Responsibilities
  • Develop novel research toward massive scale self-play for foundation model training, agentic tasks, and imbuing models with the capability to proactively learn from its environment.

  • Initiate and pursue novel reinforcement learning algorithmic approaches to define and drive emergent capabilities in Foundation Models.

  • Full-stack engineering from data curation, model architecture and algorithm design, to final production of models for end-users using high quality (documented, tested, maintainable) code.

  • Contribute to technical reports and research publications.

  • Represent MBZUAI at industry conferences and events, showcasing the institution's technology anddeep learning capabilities and establishing MBZUAI as a global leader in AI research and innovation.

  • Proactively engage with the open-source community.

  • Contribute to large-scale reinforcement learning training and inference frameworks.

  • Facilitate internal and external collaboration

Academic Qualifications
MSc/MEng or PhD Degree (or equivalent experience) in Machine Learning, Computer Science or related fields.
ProfessionalExperience
Minimum
  • 3+ years of hands-on experience with reinforcement learning

  • Demonstrated ability to independently identify limitations of current practice (internal and external), formulate and enact solution strategies for improvement.

  • Proactive mindset with the ability to identify impactful research questions and execute on them with minimal supervision.

  • Strong Python development skills with a focus on research-grade code and scalable data pipelines.

  • Practical experience implementing complex mathematical concepts into reliable, well-documented code.

  • Experience applying novel RL algorithms to practical applications.

  • Strong experience contributing to academic and/or open-source research through publication, GitHub contributions, or professional presentations.

  • Strong communication and collaboration skills for effective cross-functional work.

Preferred Qualifications
  • Strong systems and engineering expertise in deep learning frameworks such as PyTorch, Jax, etc.

  • Experience in large-scale model training (LLMs or Diffusion Models) on large clusters.

  • Familiarity with current RL+LLM training libraries

  • Experience training policies in self-play, possibly demonstrated by publication, blog post, public code.

  • Experience working with Diffusion Models in RL, possibly demonstrated by publication, blog post, public code.

  • Strong publication record in leading AI and RL venues (e.g.ICLR, ICML, NeurIPS, RLC, JMLR, TMLR)

  • Familiarity with performance constraints in production environments and the trade-offs in model design and execution.

  • Prior contributions to open-source ML research or data tools.

  • Demonstrated ability to solve complex system-level challenges and debug failures across training/inference stack (e.g. memory issues, deadlocks, I/O bottlenecks, multi-node communication failures).

$150,000 - $450,000 a year
Salary Range
The posted salary range represents the company's good faith estimate of the compensation for this position upon hire. The actual compensation offered may vary within this range depending on individual qualifications, including but not limited to relevant skills, experience, education, certifications, geographic location, and specific business needs.

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