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Postdoctoral In Reinforcement Learning Jobs in Tampa, FL

TD SYNNEX

Physical AI Engineer

Clearwater, FL · On-site

Strong background in reinforcement learning, imitation learning, or learning-based control, including domain randomization and curriculum learning. * Proven experience building simulation ...

Synnex

Physical AI Engineer

Clearwater, FL · On-site +1

Strong background in reinforcement learning, imitation learning, or learning-based control, including domain randomization and curriculum learning. * Proven experience building simulation ...

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Postdoctoral In Reinforcement Learning information

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$55.8K

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

As of Aug 31, 2026, the average yearly pay for postdoctoral in reinforcement learning in Tampa, FL is $55,777.00, according to ZipRecruiter salary data. Most workers in this role earn between $46,300.00 and $62,800.00 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 Tampa, FL?

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

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The top searched job categories for Postdoctoral In Reinforcement Learning jobs in Tampa, FL are:

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Postdoctoral In Reinforcement Learning jobs near you

Physical AI Engineer

TD SYNNEX

Clearwater, FL • On-site

Full-time

Re-posted 5 days ago

TD SYNNEX rating

7.4

Company rating: 7.4 out of 10

Based on 45 frontline employees who took The Breakroom Quiz

116th of 226 rated it services

Job description

Physical AI Engineer
Build the Future of Robotics with Physical AI
We are building real-world Physical AI systems where models interact with physical machines. This role is for a robotics engineer with a strong reinforcement learning (RL) mindset, someone who wants to train, evaluate, and deploy intelligent behaviors that emerge through interaction, not just perception, by building the virtual environments, generating the data that trains our models, and developing the policies that eventually run on real hardware.
Day to day, you will design simulation environments, produce large volumes of labeled synthetic data, train and evaluate learned policies, and work with engineers across robotics, controls, and perception to close the sim-to-real gap. You will work hands-on with NVIDIA Omniverse, Isaac Sim, physics-based simulation, and foundation models. This is a builder role: fast iteration, scalable training, and direct transfer from simulation to physical robots.
What You'll Do
  • Build and maintain high-fidelity, physics-accurate simulation environments in NVIDIA Omniverse and Isaac Sim for training, testing, and validating robotic systems.
  • Generate synthetic datasets at scale, including sensor and camera simulation, domain randomization, procedural scene variation, and automated annotation such as segmentation, depth, bounding boxes, and pose. You own dataset quality, versioning, and delivery.
  • Design and run reinforcement learning and imitation learning pipelines using simulation-generated and synthetic data.
  • Train and tune policies for control, planning, navigation, and manipulation, with emphasis on robustness and sim-to-real performance.
  • Define task curricula, reward functions, and evaluation benchmarks so policy performance is measured before it reaches hardware.
  • Model sensors, actuators, and contact behavior, and debug simulation instability, non-physical behavior, and determinism issues.
  • Drive simulation-to-real transfer through domain randomization, system identification, and validation on physical systems.
  • Build reusable tooling, APIs, and documentation so the broader team can stand up new environments and tasks without deep simulation expertise.
  • Integrate foundation models to support reasoning, task decomposition, and human-in-the-loop learning.
Requirements
  • 5+ years in robotics, reinforcement learning, simulation, or applied machine learning. Degree in Robotics, Computer Science, or a related field, or equivalent hands-on experience.
  • Hands-on experience training robotic agents in simulation, on physical systems, or both.
  • Strong background in reinforcement learning, imitation learning, or learning-based control, including domain randomization and curriculum learning.
  • Proven experience building simulation environments in NVIDIA Omniverse, Isaac Sim, or Isaac Lab, or comparable GPU-accelerated simulation platforms.
  • Direct experience generating synthetic data for model training, including sensor simulation, annotation pipelines, and large-scale dataset generation.
  • Working knowledge of OpenUSD as a robotics engineer, including asset conversion into a simulation pipeline from formats such as URDF or MJCF.
  • Production experience with at least one RL library: RSL-RL, RL-Games, skrl, or Stable-Baselines3.
  • Strong Python and the deep learning stack, such as PyTorch or JAX, with the ability to build and scale training pipelines beyond a single workstation.
  • Experience applying or integrating foundation models into robotics or decision-making workflows.
  • Builder mindset with a track record of moving learning systems from experiment to deployment.
Nice to Have
  • PhysX schemas and physics tuning.
  • MuJoCo Playground, NVIDIA Warp, or Newton.
  • Omniverse Replicator or comparable synthetic data generation frameworks.
  • World foundation models used for data augmentation and photoreal domain transfer.
  • Vision language action models or multimodal policies.
  • ROS 2 or comparable robotics middleware, real-time systems, or physics engines.
  • Model-free or model-based RL at scale, including distributed or cloud-scale training orchestration.
  • Training perception models such as detection, segmentation, or pose estimation on synthetic data.
  • C++ alongside Python for real-time robotics systems.
  • Experience operationalizing learned policies on physical robots in production environments.
Work Environment
  • Remote or hybrid, US-based, with periodic time onsite at our robotics facility.
  • Occasional domestic and global travel.
  • Flexible working hours aligned to experimentation and training cycles.

--- This description is optimized to attract senior, hands-on AI robotics engineers with strong reinforcement learning and simulation expertise.
At TD SYNNEX, our values guide everything we do: Together, We Own It, We Dare to Go, We Grow and Win, and above all, We Do the Right Thing. These principles shape how we work with each other, our partners, and our communities as we drive innovation and create lasting impact.
What's In It For You?
  • Elective Benefits: Our programs are tailored to your country to best accommodate your lifestyle.
  • Grow Your Career: Accelerate your path to success (and keep up with the future) with formal programs on leadership and professional development, and many more on-demand courses.
  • Elevate Your Personal Well-Being: Boost your financial, physical, and mental well-being through seminars, events, and our global Life Empowerment Assistance Program.
  • Diversity, Equity & Inclusion: It's not just a phrase to us; valuing every voice is how we succeed. Join us in celebrating our global diversity through inclusive education, meaningful peer-to-peer conversations, and equitable growth and development opportunities.
  • Make the Most of our Global Organization: Network with other new co-workers within your first 30 days through our onboarding program.
  • Connect with Your Community: Participate in internal, peer-led inclusive communities and activities, including business resource groups, local volunteering events, and more environmental and social initiatives.

Don't meet every single requirement? Apply anyway.
At TD SYNNEX, we're proud to be recognized as a great place to work and a leader in the promotion and practice of diversity, equity and inclusion. If you're excited about working for our company and believe you're a good fit for this role, we encourage you to apply. You may be exactly the person we're looking for!
We are an equal opportunity employer and committed to building a team that represents and empowers a variety of backgrounds, perspectives, and skills. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, gender, gender identity or expression, sexual orientation, protected veteran status, disability, genetics, age, or any other characteristic protected by law.
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