2 hours ago
Clearwater, FL, USASenior
Responsibilities
- Build and maintain high-fidelity, physics-accurate simulation environments in NVIDIA Omniverse and Isaac Sim.
- Generate large-scale synthetic datasets with sensor and camera simulation, domain randomization, procedural scene variation, and automated annotations such as segmentation, depth, bounding boxes, and pose.
- 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, emphasizing robustness and sim-to-real performance.
- Define task curricula, reward functions, and evaluation benchmarks before policies reach hardware.
- Model sensors, actuators, and contact behavior while debugging simulation instability, non-physical behavior, and determinism issues.
- Drive simulation-to-real transfer through domain randomization, system identification, and physical-system validation.
- Build reusable tooling, APIs, and documentation for creating new environments and tasks.
- Integrate foundation models for reasoning, task decomposition, and human-in-the-loop learning.
Requirements
- 5+ years of experience 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.
- Experience building simulation environments with NVIDIA Omniverse, Isaac Sim, Isaac Lab, or comparable GPU-accelerated simulation platforms.
- Direct experience generating synthetic training data, including sensor simulation, annotation pipelines, and large-scale dataset generation.
- Working knowledge of OpenUSD and asset conversion from formats such as URDF or MJCF into simulation pipelines.
- Production experience with at least one reinforcement-learning library: RSL-RL, RL-Games, skrl, or Stable-Baselines3.
- Strong Python and deep-learning-stack experience with PyTorch or JAX, including scaling training pipelines beyond a single workstation.
- Experience applying or integrating foundation models into robotics or decision-making workflows.
- Track record of moving learning systems from experimentation to deployment.
- Preferred experience with PhysX schemas and physics tuning; MuJoCo Playground, NVIDIA Warp, or Newton; Omniverse Replicator; world foundation models; vision-language-action models; ROS 2; real-time systems; physics engines; distributed or cloud-scale training; synthetic-data perception models; C++; and learned policies deployed on physical robots in production.
Benefits
- Remote or hybrid US-based work with periodic onsite time at the robotics facility.
- Occasional domestic and global travel.
- Flexible working hours aligned with experimentation and training cycles.
- Elective benefits tailored to the employee's country.
- Formal leadership and professional-development programs and on-demand courses.
- Financial, physical, and mental well-being support through seminars, events, and the global Life Empowerment Assistance Program.
- Inclusive communities, diversity and equity initiatives, peer-led activities, volunteering, and environmental and social programs.
- Onboarding opportunities to network with new coworkers within the first 30 days.
Categories
ML EngineeringRobotics
