Physical AI Engineer
Synnex Open · verified Oct 22, 2026 At a glance
Mid-level AI Engineering role at Synnex. Clearwater · full-time · no salary stated · we estimate $132,450–$205,151 across the United States.
Synnex has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build physics-accurate robot simulations in Omniverse and Isaac Sim for training, testing and validation
- Create large synthetic sensor datasets, automate annotations, and manage dataset quality, versions and delivery
- Train and assess reinforcement and imitation learning policies for robot control, navigation and manipulation
- Set learning tasks, reward rules and benchmarks, then check policy robustness and transfer to physical robots
- Develop reusable simulation tools and integrate foundation models for reasoning, task breakdown and human-guided learning
What you bring
- At least five years in robotics, reinforcement learning, simulation or applied machine learning
- A robotics or computer science degree, a related qualification, or equivalent hands-on experience
- Practical experience training robot agents in simulation or on physical systems, with reinforcement or imitation learning
- Built GPU-accelerated robot simulations and synthetic-data pipelines, including sensor modeling and automated annotation
- Production use of an RL library, plus strong Python and PyTorch or JAX skills for scalable training
Who this fits
You’ll suit hands-on robotics engineers who can carry learning systems from experimentation toward deployment. The work is remote or hybrid in the United States, with periodic onsite time at a robotics facility and occasional domestic and international travel. Hours can flex around experimentation and training cycles.
From the employer
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.
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What’s In It For You?
- Elective Benefits: Our programs are tailored to your country to best accommodate your lifestyle.
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