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Senior Reinforcement Learning Jobs in Florida (NOW HIRING)

Flexible working hours aligned to experimentation and training cycles. --- This description is optimized to attract senior, handson AI robotics engineers with strong reinforcement learning and ...

As a Senior Associate, you will focus on building meaningful client connections and learning how to ... and reinforcement learning agents - Applying natural language processing techniques for text ...

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As a Senior Associate, you will focus on building meaningful client connections and learning how to ... and reinforcement learning agents - Applying natural language processing techniques for text ...

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DATA SCIENTIST LEAD

Tampa, FL · On-site

$104K - $169K/yr

... and reinforcement learning). Mentors and trains junior and professional staff members. Data ... Individuals may report concerns or questions to UAB's Assistant Vice President and Senior Title IX ...

Serve as the senior technical advisor on data science, machine learning, and analytics capabilities ... Apply supervised, unsupervised, reinforcement learning, and generative AI techniques, including ...

Senior ML Engineer

Dania Beach, FL · On-site

$102K - $141K/yr

Implement and experiment with reinforcement learning from human feedback (RLHF) workflows, including PPO (Proximal Policy Optimization) and GRPO (Group Relative Policy Optimization) for model ...

The Data Scientist will frequently interface with senior stakeholders, providing thought leadership ... Reinforcement Learning. * Implement and maintain feature stores, model monitoring workflows, and ...

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Senior Reinforcement Learning information

What are some common challenges faced by senior reinforcement learning professionals when deploying models in real-world environments?

Senior Reinforcement Learning professionals often encounter challenges such as ensuring model robustness when transferring algorithms from simulated to real-world environments, handling limited or noisy data, and managing the computational demands of training complex models. Additionally, safety and interpretability are critical, as real-world deployments can have significant impacts if models behave unpredictably. Close collaboration with domain experts and engineering teams is essential to address these challenges and ensure successful, scalable deployments.

What are the key skills and qualifications needed to thrive as a senior reinforcement learning engineer, and why are they important?

To thrive as a Senior Reinforcement Learning Engineer, you need deep expertise in machine learning, reinforcement learning algorithms, and programming languages such as Python, often supported by an advanced degree in computer science or a related field. Familiarity with frameworks like TensorFlow, PyTorch, and RL-specific libraries, as well as experience with high-performance computing and cloud platforms, is typically required. Strong problem-solving abilities, collaboration, and communication skills help distinguish top performers in this role. These skills ensure the development of efficient, robust RL models and effective teamwork on complex AI projects.

What is the difference between Senior Reinforcement Learning vs Data Scientist?

AspectSenior Reinforcement LearningData Scientist
Required CredentialsAdvanced degrees in CS, ML, or related fields; experience with RL frameworksDegree in CS, Statistics, or related; strong analytical skills
Work EnvironmentResearch labs, AI teams, tech companies focusing on ML projectsBusiness analytics, data analysis, and modeling in various industries
Employer & Industry UsageTech firms, AI startups, research institutionsFinance, healthcare, marketing, tech, and more

While both roles require strong analytical skills and technical knowledge, Senior Reinforcement Learning specialists focus on developing RL algorithms and models, often in AI research settings. Data Scientists analyze data to inform business decisions across industries. The roles overlap in data handling and programming but differ in their core focus and application areas.

What does a senior reinforcement learning engineer do?

A Senior Reinforcement Learning Engineer designs, develops, and implements advanced machine learning algorithms that enable systems to learn optimal behaviors through trial and error. They work on complex problems such as robotics, game AI, recommendation systems, and automated decision-making. In addition to coding and model development, they often lead research initiatives, collaborate with cross-functional teams, and mentor junior engineers. Their role requires deep knowledge of reinforcement learning theory, practical experience with machine learning frameworks, and strong programming skills.
What are the most commonly searched types of Reinforcement Learning jobs in Florida? The most popular types of Reinforcement Learning jobs in Florida are:
What cities in Florida are hiring for Senior Reinforcement Learning jobs? Cities in Florida with the most Senior Reinforcement Learning job openings:

Physical AI Engineer

Synnex

Clearwater, FL • On-site, Remote

Full-time

Re-posted 14 days ago


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, handson AI robotics engineers with strong reinforcement learning and simulation expertise.

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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.
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  • 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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