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Postdoctoral In Reinforcement Learning Jobs in Santa Clara, CA

JazzX AI

Senior Staff AI Engineer

Los Altos, CA · On-site

$123K - $169K/yr

Required : • 10+ years of experience in AI/ML engineering, including at least 5 years specializing in reinforcement learning research and production systems. • Demonstrated success in designing ...

JazzX AI

Senior Staff AI Engineer

Los Altos, CA · On-site

$123K - $169K/yr

Required : • 10+ years of experience in AI/ML engineering, including at least 5 years specializing in reinforcement learning research and production systems. • Demonstrated success in designing ...

1X

Research Engineer, Reinforcement Learning

Palo Alto, CA · On-site

$130K - $250K/yr

Join to apply for the Research Engineer, Reinforcement Learning role at 1X Join to apply for the ... Since its founding in 2015, 1X has been at the forefront of developing advanced humanoid robots ...

1X

Research Engineer, Reinforcement Learning

Palo Alto, CA · On-site

$130K - $250K/yr

Join to apply for the Research Engineer, Reinforcement Learning role at 1X Join to apply for the ... Since its founding in 2015, 1X has been at the forefront of developing advanced humanoid robots ...

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Postdoctoral In Reinforcement Learning information

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$29.4K

$69.3K

$98.1K

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 Santa Clara, CA is $69,317.00, according to ZipRecruiter salary data. Most workers in this role earn between $57,500.00 and $78,100.00 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.

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Postdoctoral In Reinforcement Learning jobs near you

Reinforcement Learning Engineer

HammerheadAI

Redwood City, CA • On-site

Full-time

Medical, Dental, Vision, Retirement

Re-posted 7 days ago

Job description

About Hammerhead
We're unleashing AI with intelligent orchestration while addressing one of the most pressing bottlenecks for AI access to Power. Our cutting-edge platform optimizes data center power infrastructure to maximize AI token generation within existing electrical limits, without requiring new power plants or grid expansions. Our team has optimized over 8 gigawatts of mission-critical power globally, and we're addressing a $64 billion-per-year market opportunity while dramatically reducing the environmental footprint of AI infrastructure.
At Hammerhead, you will:
Work at the intersection of AI, energy, and compute creating the next generation AI infrastructure
Collaborate with colleagues that are experts in modern RL and AI, IoT and IIoT software, and infrastructure technologies
Contribute to building a more efficient and sustainable future for AI compute.
Join a company at the cutting edge of modern data center design and operation
Receive competitive compensation, equity, and benefits in a high-growth, mission-driven environment.
Learn from an experienced team that has built and sold startups before
Learn more about Hammerhead
  • These AutoGrid alums want to change how data centers use power
  • How Hammerhead Wants to Rewrite the Economics of AI
  • News & Blogs

Role Description
As a Reinforcement Learning Engineer, you will be the architect of the core intelligence for Hammerhead's ORCA platform. Reporting to the Head of AI / Reinforcement Learning Engineering, you will design, train, and deploy the Orchestrated RL Control Agents that form the brain of our system, making real-time decisions to optimize power and compute resources across physical data centers. This role is for a hands-on expert who is passionate about applying cutting-edge RL research to complex, real-world industrial systems. You will be instrumental in developing the models that control physical assets like cooling systems and power distribution units to unlock massive efficiency gains in AI workloads.
Key Responsibilities
  • RL Model Development: Design and implement advanced reinforcement learning algorithms (e.g., multi-agent RL, model-based RL, deep RL) for real-time control of data center infrastructure.
  • Simulation and Training: Build and train RL agents that can generalize to real-world, physical systems.
  • From Lab to Production: Lead the transition of RL models from research and simulation to live deployment within the ORCA platform, ensuring stability and performance on mission-critical hardware.
  • System Optimization: Analyze agent performance to continuously improve control strategies for tasks like peak shaving, workload shifting, and thermal management.
  • Cross-Functional Collaboration: Partner with platform engineers to define the APIs, data telemetry, and infrastructure needed to support and scale our RL agents across a global portfolio of data centers.

Qualifications
  • RL Expertise: Proven experience developing and implementing reinforcement learning algorithms, demonstrated through publications in top conferences (e.g., NeurIPS, ICML, ICLR), open-source contributions, or shipped products.
  • Industry Experience: 3+ years of experience applying RL to real-world problems, preferably in industrial automation, robotics, autonomous vehicles, energy systems, or other physical systems. Experience from a leading industrial or academic RL lab is highly desirable.
  • Technical Skills: Deep proficiency in Python and modern ML frameworks such as PyTorch, Jax, or TensorFlow. Experience with simulation platforms and RL libraries (e.g., Ray RLlib, Isaac Gym) is a plus.
  • Educational Background: MS or PhD in Computer Science, Robotics, Operations Research, or a related field with a focus on machine learning or control theory.
  • Problem Solver: You possess a strong theoretical background but are driven by practical application, with an ability to bridge the gap between RL theory and the constraints of physical, real-world systems.

What We Offer
  • Competitive salary, bonus, 401(k) plan and equity in a rapidly growing startup
  • Comprehensive health, dental, and vision coverage
  • Opportunity to apply the latest AI technologies working with an experienced team

Join our team to shape the foundation of tomorrow's AI infrastructure
Visit our Careers page at (hammerheadco dot ai / careers) to apply

Apply