Robotics AI Engineer: Sim-to-Real RL & Simulation

TD SYNNEX North America; Clearwater (FL); Hybrid; USD 120,000 - 180,000; Full time

Job summary

TD SYNNEX North America is seeking a senior robotics engineer with a strong reinforcement learning mindset to design, train, and deploy intelligent behaviors on real hardware.

You will build high-fidelity simulation environments, generate synthetic data, and develop policies that bridge sim-to-real gaps. This role emphasizes hands-on work with NVIDIA Omniverse, Isaac Sim, and open-source RL toolchains, with opportunities for deployment to physical robots.

Qualifications

• 5+ years in robotics, reinforcement learning, simulation, or applied machine learning.
• 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.

Responsibilities

• 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.
• 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.

Skills

Robotics

Reinforcement learning

Simulation environments

Python

PyTorch

OpenUSD

C++

ROS 2

Domain randomization

Curriculum learning

Education

Degree in Robotics, Computer Science, or related field

Tools

NVIDIA Omniverse

Isaac Sim

RL libraries (Stable-Baselines3)

PyTorch

Job description

TD SYNNEX North America is seeking a senior robotics engineer with a strong reinforcement learning mindset to design, train, and deploy intelligent behaviors on real hardware.

You will build high-fidelity simulation environments, generate synthetic data, and develop policies that bridge sim-to-real gaps. This role emphasizes hands-on work with NVIDIA Omniverse, Isaac Sim, and open-source RL toolchains, with opportunities for deployment to physical robots.