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Remote Nvidia Engineering Jobs in Florida (NOW HIRING)

You will work hands-on with NVIDIA Omniverse, Isaac Sim, physics-based simulation, and foundation ... Remote or hybrid, US-based, with periodic time onsite at our robotics facility. * Occasional ...

Remote Nvidia Engineering information

What is a remote Nvidia engineer?

A Remote Nvidia Engineer is a professional who works for Nvidia, or with Nvidia technologies, from a location outside of a traditional office setting. These engineers may specialize in areas such as GPU development, AI research, software engineering, or hardware design, and they collaborate with teams virtually. Remote Nvidia Engineers use digital tools to communicate, manage projects, and contribute to cutting-edge technologies in graphics processing, artificial intelligence, and computing platforms. The remote aspect allows for flexible work arrangements and the ability to participate in global projects.

What are some common challenges faced by engineers working remotely for Nvidia, and how can they be overcome?

Remote engineers at Nvidia often encounter challenges related to communication across time zones, staying aligned with fast-paced project developments, and maintaining visibility within distributed teams. To overcome these, it's important to proactively engage in virtual meetings, leverage collaboration tools like Slack and Jira, and regularly update your team on progress. Building strong relationships with peers and seeking out mentorship opportunities can also help remote engineers stay connected and advance within the company.

What are the key skills and qualifications needed to thrive as a remote Nvidia engineer, and why are they important?

To excel as a Remote Nvidia Engineer, you typically need a strong background in computer engineering, programming (e.g., C++, Python), and experience with GPU architectures, often supported by a relevant degree. Familiarity with Nvidia tools like CUDA, cuDNN, and deep learning frameworks, as well as proficiency in remote collaboration platforms, are crucial. Strong problem-solving skills, self-motivation, and effective communication are vital soft skills for working independently and collaborating across distributed teams. These competencies ensure efficient development, troubleshooting, and innovation in Nvidia's complex, high-performance computing environments.

What is the difference between Remote Nvidia Engineering vs Remote Nvidia Data Scientist?

AspectRemote Nvidia EngineeringRemote Nvidia Data Scientist
Required CredentialsBachelor's in Engineering, Computer Science, or related field; experience with GPU programmingBachelor's or higher in Data Science, Statistics, or related; proficiency in machine learning and data analysis
Work EnvironmentDesign, develop, and optimize GPU hardware/software; collaborative teamsAnalyze large datasets, develop models, and generate insights; often cross-functional teams
Employer & Industry UsagePrimarily in hardware, AI, and high-performance computing sectorsPrimarily in AI, analytics, and research sectors

Remote Nvidia Engineering focuses on hardware and software development for GPUs, requiring engineering credentials and technical skills. Remote Nvidia Data Scientists analyze data and build models, requiring expertise in data science. Both roles are remote, but they serve different functions within Nvidia's ecosystem.

What are the most commonly searched types of Nvidia Engineering jobs in Florida? The most popular types of Nvidia Engineering jobs in Florida are:
What cities in Florida are hiring for Remote Nvidia Engineering jobs? Cities in Florida with the most Remote Nvidia Engineering job openings:
Infographic showing various Remote Nvidia Engineering job openings in Florida as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% Remote job distribution.

Physical AI Engineer

Synnex

Clearwater, FL • On-site, Remote

Full-time

Re-posted 13 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.

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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