Virtual Reference Library Jobs in Salem, MA (NOW HIRING)

Your Impact at LILA

Lila Sciences is building autonomous science platforms that compress the cycle time of scientific discovery. The Next Gen Robotics team, within the Robotics department, is building the foundational simulation and robotic infrastructure that will power the AI Science Factory (AISF) at our 5AP facility. As we ramp toward first science later this year, simulation is becoming a load-bearing capability across both robotics production and orchestration.

We are looking for a Simulation Engineer to help establish the foundation of our simulation platform. You will be a core technical contributor shaping how we design, build, and scale virtual environments - from individual scene authoring to automated, headless data generation pipelines. You will work across the full simulation stack: scene infrastructure, physics and sensor fidelity, programmatic scene construction, and sim-to-real transfer workflows.

This is a high-impact, foundational role on a team being built from the ground up. You will have significant influence over tooling decisions, architectural patterns, and best practices, and your work will directly de-risk robotics development and accelerate throughput modeling for orchestration.

What You'll Be Building

• Design and maintain modular, reusable simulation scene libraries using well-structured USD (Universal Scene Description), including proper use of references, payloads, variants, layers, and composition arcs.
• Build Python-based tooling and automation around the Omniverse Kit API (omni.usd, omni.kit, omni.replicator) to enable programmatic scene construction, editing, and batch processing, including headless Isaac Sim workflows for large-scale automated simulation runs and synthetic data generation.
• Contribute to internal simulation SDKs, APIs, and developer tools that allow other engineers to build on top of the simulation platform.
• Prototype and evaluate new simulation techniques, including physics calibration, sensor modeling, environment generation, and GPU-accelerated motion planning (e.g., cuRobo) for inverse kinematics and collision-aware trajectory generation.
• Design and implement domain randomization strategies (appearance, physics, lighting, sensor noise) using NVIDIA Replicator, and build tooling to evaluate and benchmark sim-to-real performance across configurations.
• Collaborate with ML and robotics teams to characterize and reduce the sim-to-real gap, including physics parameter tuning, contact modeling, and sensor simulation fidelity.

What You'll Need to Succeed

• 3-5 years of hands-on experience in simulation engineering, robotics software, or a closely related field.
• Proficiency with NVIDIA Isaac Sim and the Omniverse Kit framework, including Python scripting via the Omniverse API (omni.usd, omni.kit.app, omni.physx).
• Solid understanding of USD (Universal Scene Description), including authoring modular scenes with references, payloads, variants, sublayers, and composition arcs.
• Familiarity with robot kinematics concepts (forward/inverse kinematics, motion planning, joint control) and implementing them programmatically in a simulation environment.
• Strong Python skills; comfort writing clean, maintainable, production-quality code.
• Experience with physics simulation concepts (rigid body dynamics, contact/friction modeling, articulations).

Bonus Points For

• Experience running Isaac Sim in headless mode for automated, script-driven simulation workflows, and with distributed or cloud-based simulation workloads.
• Experience with cuRobo or similar GPU-accelerated motion generation libraries (CUDA-based trajectory optimization, batched IK), and motion planning frameworks like MoveIt2 or OMPL.
• Familiarity with NVIDIA Replicator for synthetic data generation and domain randomization, OmniGraph for node-based compute, and Isaac Lab for RL-based training environments.
• Experience with ROS/ROS2 and sensor simulation (cameras, depth, LiDAR), including real-to-sim workflows (photogrammetry, NeRF, CAD asset pipelines).
• C++ experience relevant to simulation extensions or performance-critical tooling.