At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing. We believe massive scale through data-driven machine learning is the key to unlocking these capabilities for the widespread deployment of robots within society. Our team consists of individuals with varying levels of experience and backgrounds, from new graduates to domain experts. Relevant industry experience is important, but ultimately less so than your demonstrated abilities and attitude. We are looking for passionate individuals who are eager to explore uncharted waters and contribute to our innovative projects.
We are seeking a Physics Simulation Scientist to lead advancements in the simulation and physics-solving backbone behind Skild’s robot foundation model training. You will collaborate with external experts in GPU-accelerated physics engines and work with our internal robotics and learning teams to build a next-generation, open-source simulation stack for robotics sim-to-real.
You’ll partner closely with engineers scaling simulation scene generation and with ML researchers pushing the limits of sim-to-real transfer. The ideal candidate brings deep physics-simulation expertise plus hands-on experience implementing and optimizing algorithms on modern GPUs.
Responsibilities- Improve and develop new physics solvers and modeling methods for high-DoF, contact-rich robotics tasks.
- Design and implement GPU-accelerated solvers with a focus on throughput, stability, and scalability.
- Profile and optimize simulation performance on modern GPU hardware and distributed clusters.
- Work with external collaborators and the open-source community to advance simulation for robotics.
- Collaborate with scene-generation engineers to scale robotic experience across diverse real-world environments.
- Partner with ML researchers to improve sim-to-real transfer through better physical modeling, calibration, and training regimes.
- Contribute to the long-term technical direction of Skild’s physical modeling and sim-to-real strategy.
- MS or PhD in Physics, Robotics, Computer Science, Applied Math, Engineering, or a related field, or equivalent hands-on experience.
- Strong track record working on physics engines or high-fidelity simulators for articulated rigid bodies; experience with deformables, fluids, or differentiable simulation is a plus.
- Deep understanding of dynamics, contact modeling, constraint-based methods, and integrators, including accuracy–speed tradeoffs.
- Expertise in CUDA and GPU programming with proven ability to optimize for scale.
- Proficiency in C++ and Python, and experience building reliable systems used by other technical teams.
- Familiarity with how modern ML/RL pipelines consume simulation (vectorized environments, domain randomization, large-scale rollouts).
- Experience with real robot platforms and strong intuition for where simulation diverges from reality.
- Publications, open-source contributions, or shipped systems in simulation, robotics, graphics, or numerical computing are a strong plus.
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What We Do
Building general purpose robotic intelligence
