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Postdoctoral In Reinforcement Learning Jobs in California

Dexmate

Reinforcement learning engineer

Santa Clara, CA · On-site

Responsibilities : • Design and implement reinforcement learning algorithms for various robotics tasks • Develop and optimize RL training pipelines in both simulation and real-world environments ...

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 ...

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Postdoctoral In Reinforcement Learning information

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 are popular job titles related to Postdoctoral In Reinforcement Learning jobs in California?

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Postdoctoral In Reinforcement Learning jobs near you

AI Research Scientist, Reinforcement Learning (LLM) and Post-Training

Advanced Micro Devices, Inc

Santa Clara, CA • On-site

$178K/yr

Full-time

Re-posted 9 days ago

Advanced Micro Devices rating

8.6

Company rating: 8.6 out of 10

Based on 13 frontline employees who took The Breakroom Quiz

27th of 159 rated electronics manufacturers

Job description

ADVANCE YOUR CAREER. ADVANCE THE WORLD.
At AMD, we believe technology has the power to solve the world's most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future.
Whether you're designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger - technology that moves the world forward. Join us and, together, we'll advance your career.
THE ROLE:
We are hiring a AI Research Scientist, Reinforcement Learning (LLM) and Post-Training, specializing in reinforcement learning to advance post-training and interactive learning for large generative models applied to demanding engineering and hardware-adjacent tasks (code, optimization, tool use, and long-horizon decision making). You will invent and analyze RL algorithms-policy optimization, preference-based methods, exploration, credit assignment, and reward modeling-run rigorous empirical studies, and partner with infra and product teams to land methods that improve measurable task success without sacrificing stability or safety.
THE PERSON:
You publish and ship. You are fluent in both RL theory and the practical path from ablation to production-scale training. You care about reward misspecification, variance reduction, and evaluation that reflects real constraints-not only toy environments.
KEY RESPONSIBILITIES:
  • Research and develop RL methods for post-training LLMs and code models on structured engineering tasks with verifiable or preference-based feedback
  • Design reward models, curricula, and off-policy or on-policy training recipes suited to sparse, noisy, or expensive labels from experts and simulators
  • Characterize failure modes (reward hacking, degenerate policies, instability) and propose mitigations grounded in experiments
  • Collaborate with RL infra engineers to scale training; define interfaces for rollout generation, logging, and reproducibility
  • Publish at top venues (e.g. NeurIPS, ICML, ICLR) and contribute internal technical leadership on the RL roadmap

PREFERRED EXPERIENCE:
  • Strong publication record in reinforcement learning or closely related machine learning areas.
  • Hands-on experience training RL or preference-optimized models at non-trivial scale (GPUs, distributed jobs)
  • Experience with LLM post-training, RLHF/RLAIF, or policy optimization for language or code agents
  • Familiarity with compilers, kernels, EDA-style workflows, or large-scale codebases is a plus

ACADEMIC CREDENTIALS:
  • PhD in Computer Science, Machine Learning, or related field strongly preferred.

#LI-BM1
#LI-Hybrid
Benefits offered are described: AMD benefits at a glance.
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants' needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD's "Responsible AI Policy" is available here.
This posting is for an existing vacancy.

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