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Postdoctoral In Reinforcement Learning Jobs in Pittsburgh, PA

Skild AI

Research Scientist

Pittsburgh, PA · On-site

$100K - $300K/yr

Deep technical knowledge, and research experience in deep learning, reinforcement learning, robotics, or computer vision. * Deep understanding of state-of-the-art machine learning techniques and ...

Duolingo

Staff AI Research Engineer

Pittsburgh, PA

... tuning, reinforcement learning, quality evaluations, deployment, and monitoring. On the ... Stay up-to-date with the latest developments in machine learning, particularly in LLMs and bandits ...

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Postdoctoral In Reinforcement Learning information

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$24.3K

$57.3K

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

As of Aug 19, 2026, the average yearly pay for postdoctoral in reinforcement learning in Pittsburgh, PA is $57,299.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,600.00 and $64,600.00 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 are popular job titles related to Postdoctoral In Reinforcement Learning jobs in Pittsburgh, PA?

For Postdoctoral In Reinforcement Learning jobs in Pittsburgh, PA, the most frequently searched job titles are:

What job categories do people searching Postdoctoral In Reinforcement Learning jobs in Pittsburgh, PA look for?

The top searched job categories for Postdoctoral In Reinforcement Learning jobs in Pittsburgh, PA are:

Postdoctoral In Reinforcement Learning jobs near you

Post Doctoral Associate

University of Pittsburgh

Pittsburgh, PA

Full-time

Re-posted 8 days ago

Job description

The University of Pittsburgh School of Medicine's Department of Dermatology is recruiting Postdoctoral Associates with interests in Bioengineering. This is an exciting opportunity for individuals who thrive in an interesting, challenging, and highly collaborative work environment.  Postdoctoral Associates will contribute to the department by focusing on developing clinically translatable biomedical technologies for skin vaccination and wearable diagnostics, as well as by validating these new technologies using human skin models and in vivo animal models. 

The Postdoctoral Associates will carry out advanced independent and/or directed research to achieve the objectives of the research projects. They will work in multiple multidisciplinary projects and will have opportunities to collaborate with distinguished teams of PIs and other postdoctoral associates. They will help mentor junior trainees in related areas and assist in the management of resources and equipment used in engineering and in vitro and in vivo characterization of these next-generation skin-targeted biomedical technologies. 

Qualifications:

        Design, manufacturing, and structural, electrical, and biological evaluation of biomedical technologies.

      PhD in Biomedical Engineering, Mechanical Engineering, Materials Science and Engineering, or a related field. 

      Expertise in biomimicry, biofabrication techniques, biomaterials, biosensors, wearable electronics, and machine learning. 

      Experience in mechanical and rheological testing of biodegradable materials.

      Experience in working with mouse models, cell and tissue cultures, cellular biology, and qPCR, ELISA and Flow Cytometry-based assays.  

      A publication record in peer-reviewed journals.

      Strong analytical skills.

      Excellent written and verbal communication skills. 

      A proven track record of designing, conducting, and analyzing impactful research experiments.

Responsibilities: 

      Design, manufacturing, and structural, electrical, and biological evaluation of biomedical technologies.

      Conduct research experiments and animal studies within the predetermined research scope and methodology.

      Collect, analyze, and interpret data.

      Assist in mentoring junior trainees working in the projects. 

      Attend weekly meetings to discuss the research plan and progress. 

      Prepare quarterly reports on the progress of the projects, including written documents and presentation slides. 

      Write papers and proposals. 

      Other tasks as identified pertaining to satisfying the project goals. 

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