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Postdoctoral In Reinforcement Learning Jobs in Alameda, CA

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Postdoctoral In Reinforcement Learning information

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$28.3K

$66.9K

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How much do postdoctoral in reinforcement learning jobs pay per year?

As of Aug 22, 2026, the average yearly pay for postdoctoral in reinforcement learning in Alameda, CA is $66,893.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,500.00 and $75,400.00 per year, depending on experience, location, and employer.

What is a postdoctoral researcher in reinforcement learning?

A Postdoctoral Researcher in Reinforcement Learning is an individual who has completed a PhD and conducts advanced research in the field of reinforcement learning, a branch of artificial intelligence focused on how agents take actions in environments to maximize rewards. These researchers often work in academic, industrial, or governmental research settings, collaborating on projects that advance the theoretical foundations or practical applications of reinforcement learning. Their responsibilities may include designing experiments, developing algorithms, publishing papers, and mentoring graduate students.

What are the key skills and qualifications needed to thrive as a postdoctoral researcher in reinforcement learning?

To thrive as a Postdoctoral Researcher in Reinforcement Learning, you need a PhD in computer science or a related field, with deep expertise in machine learning, statistics, and algorithm development. Proficiency in programming languages such as Python, experience with deep learning frameworks (e.g., TensorFlow or PyTorch), and familiarity with reinforcement learning libraries are typically required. Strong analytical thinking, problem-solving ability, collaboration, and scientific communication skills help you excel in research teams and publish impactful work. These competencies are vital to advancing state-of-the-art research, developing novel algorithms, and contributing to the academic and industrial progress in AI.

What are some common challenges faced by postdoctoral researchers in reinforcement learning, and how can they be addressed?

Postdoctoral researchers in reinforcement learning often face challenges such as balancing independent research projects with collaborative work, staying up-to-date with rapidly evolving literature, and managing the pressure to publish in top conferences. Effective time management, regular engagement with the research community through seminars and workshops, and seeking mentorship from senior colleagues can help address these challenges. Additionally, collaborating with interdisciplinary teams can offer fresh perspectives and support, making it easier to navigate complex research problems.

What is the difference between Postdoctoral In Reinforcement Learning vs Postdoctoral In Machine Learning?

AspectPostdoctoral In Reinforcement LearningPostdoctoral In Machine Learning
Required CredentialsPhD in Computer Science, AI, or related field; strong programming skills; research experience in reinforcement learningPhD in Computer Science, AI, or related field; strong programming skills; research experience in machine learning
Work EnvironmentAcademic labs, research institutions, industry R&D teams focused on reinforcement learning applicationsAcademic labs, research institutions, industry R&D teams working on various machine learning techniques
Industry UsagePrimarily in AI research, robotics, gaming, and autonomous systemsBroader applications including data analysis, predictive modeling, and AI research

Postdoctoral In Reinforcement Learning specializes in research related to decision-making algorithms and autonomous systems, whereas Postdoctoral In Machine Learning covers a wider range of AI techniques. Both roles require similar credentials but differ in focus and application areas.

What cities near Alameda, CA are hiring for Postdoctoral In Reinforcement Learning jobs?

Cities near Alameda, CA with the most Postdoctoral In Reinforcement Learning job openings:

Postdoctoral In Reinforcement Learning jobs near you
Infographic showing various Postdoctoral In Reinforcement Learning job openings in Alameda, CA as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 25% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $66,893 per year, or $32.2 per hour.

Research Engineer, Performance RL (Reinforcement Learning)

Menlo Ventures

San Francisco, CA • On-site

$350 - $850/hr

Other

PTO

Posted 4 days ago

Job description

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the RL Teams

Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We contribute to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.6 and Opus 4.6. Our work spans several key areas:

  • Developing systems that enable models to use computers effectively
  • Advancing code generation through reinforcement learning
  • Pioneering fundamental RL research for large language models
  • Building scalable RL infrastructure and training methodologies
  • Enhancing model reasoning capabilities

We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implementing our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting‑edge research and engineering excellence, with a deep commitment to building high‑quality, scalable systems that push the boundaries of what AI can accomplish.

About the Role

We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to safely write correct, fast code for accelerators.

Responsibilities
  • Invent, design and implement RL environments and evaluations.
  • Conduct experiments and shape our research roadmap.
  • Deliver your work into training runs.
  • Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.
Requirements

You may be a good fit if you:

  • Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch).
  • Have worked across the stack – kernels, model code, distributed systems.
  • Know how to balance research exploration with engineering implementation.
  • Are passionate about AI's potential and committed to developing safe and beneficial systems.

Strong candidates may also have:

  • Experience with reinforcement learning.
  • Experience porting ML workloads between different types of accelerators.
  • Familiarity with LLM training methodologies.
Compensation

Annual Salary: $350,000 - $850,000 USD

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. Some roles may require more time in our offices.

Visa sponsorship: We sponsor visas where possible and will make reasonable efforts to obtain a visa if you receive an offer.

Benefits: We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in San Francisco.

How we’re different

We believe that the highest-impact AI research will be big science. Our team works as a single cohesive unit on large-scale research efforts and values impact over smaller puzzles. We prioritize communication skills and collaboration.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits. Learn about our policy for using AI in our application process.

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