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Machine Learning Robotics Jobs in Florida (NOW HIRING)

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.

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.

Establish a multi-year roadmap for AI, machine learning, generative AI, workflow automation, and robotic process automation (RPA). * Identify opportunities to improve administrative efficiency ...

A knowledge of machine learning is also desired to build efficient and accurate data pipelines to ... Develop predictive and prescriptive modeling, natural language processing (NLP), Robotic Process ...

A knowledge of machine learning is also desired to build efficient and accurate data pipelines to ... Develop predictive and prescriptive modeling, natural language processing (NLP), Robotic Process ...

... Robotics - At least one of the following: Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or ...

... Robotics - At least one of the following: Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or ...

... Robotics - At least one of the following: Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or ...

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Embedded Software Engineer

Miramar, FL · On-site

$110K - $135K/yr

Key Responsibilities Computer Vision & Machine Learning * Develop, train, and deploy object ... Support adjacent automation programs, including vision-guided robotic material handling. Required ...

... Robotics - At least one of the following: Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or ...

Showing results 41-60

Machine Learning Robotics information

See Florida salary details

$19.1K

$31.8K

$65.8K

How much do machine learning robotics jobs pay per year?

As of Sep 9, 2026, the average yearly pay for machine learning robotics in Florida is $31,822.00, according to ZipRecruiter salary data. Most workers in this role earn between $24,300.00 and $34,400.00 per year, depending on experience, location, and employer.

What is a machine learning robotics?

A Machine Learning Robotics job involves developing algorithms that enable robots to learn from data and improve their performance over time. Professionals in this field work on applications such as autonomous navigation, robotic perception, and human-robot interaction. They use techniques like deep learning, reinforcement learning, and computer vision to enhance a robot's ability to understand and interact with its environment. This role typically requires expertise in machine learning, robotics, and software development, along with strong problem-solving skills.

What are the key skills and qualifications needed to thrive in machine learning robotics?

To excel in Machine Learning Robotics, a strong background in computer science, robotics, and machine learning algorithms, often supported by a relevant degree (such as in engineering or computer science), is essential. Familiarity with programming languages like Python or C++, frameworks such as TensorFlow or ROS (Robot Operating System), and experience with simulation tools are highly valuable, and certifications in AI or robotics can further enhance employability. Strong problem-solving skills, effective communication, and the ability to work collaboratively in cross-disciplinary teams help professionals stand out. These capabilities are crucial for designing, developing, and refining intelligent robotic systems that perform reliably in real-world environments.

What are some common challenges faced by professionals working in machine learning robotics?

Professionals in Machine Learning Robotics often encounter challenges like integrating machine learning models with robotic hardware, ensuring reliable performance in unpredictable real-world settings, and managing computational limitations on embedded systems. Addressing these challenges usually requires creative problem-solving and close collaboration with hardware engineers, software developers, and data scientists. You may also need to continuously refine models and testing processes based on real-time feedback and evolving project goals. This dynamic environment makes the work both demanding and highly rewarding for those eager to push the boundaries of automation and intelligent systems.

What are the most commonly searched types of Machine Learning Robotics jobs in Florida?

The most popular types of Machine Learning Robotics jobs in Florida are:

Infographic showing various Machine Learning Robotics job openings in Florida as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 22% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $31,822 per year, or $15.3 per hour.

Physical AI Engineer

Clearwater, FL • On-site

TD SYNNEX
IT Services • 10K+ employees

Full-time

Re-posted 14 days ago


TD SYNNEX rating

7.6

Company rating: 7.6 out of 10

Based on 46 frontline employees who took The Breakroom Quiz

103rd 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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