The RoleOur system runs a Multi Agent Action Expert architecture: classical precision algorithms orchestrated alongside learned policies. The job is systematically expanding the learned components while keeping the system production-safe. You own the full lifecycle of learned components on OG-1: from data collection and model training through edge deployment on Jetson AGX Orin. Every research project will have a deployment milestone. This is not a lab position.
What you will own?- Define the technical roadmap for Robot Learning and Embodied AI.
- Build and deploy learned policies for real-world mobile manipulation and contact-rich tasks.
- Develop imitation learning, reinforcement learning, VLA, and learning-from-demonstration systems.
- Fine-tune and adapt open-source VLA/foundation models for our robot platform.
- Build scalable teleoperation 1 dataset 1 training 1 evaluation 1 deployment loops.
- Develop DAgger / HG-DAgger and human-in-the-loop data collection pipelines.
- Build simulation environments and training pipelines using NVIDIA Isaac Sim / Isaac Lab.
- Develop sim-to-real strategies including domain randomization, system identification, and real-world policy adaptation.
- Explore world models and latent dynamics models for planning, prediction, and policy learning.
- Integrate learned policies with our existing ROS2 perception, planning, manipulation, control, and safety stack.
- Optimize inference for deployment on edge GPUs using TensorRT, ONNX, CUDA, profiling, quantization, and related techniques.
- Debug policies on physical robots: latency, observation drift, calibration errors, distribution shift, contact instability, action representation, control frequency, and hardware-induced failures.
- Establish rigorous evaluation for learned systems across simulation, replay datasets, and physical robot experiments.
- Build and mentor the Robot Learning team as we scale.
Our broader robot architecture already spans perception, manipulation, planning, ROS integration, simulation, force/impedance control, quality assessment, and autonomous recovery.
Requirements- BS/MS/PhD in CS, Robotics, ML, or related field from Stanford, MIT, UC Berkeley, CMU, Georgia Tech, ETH Zfcrich, or UPenn, or equivalent exceptional experience shipping learned systems on physical robots.
- PhD: minimum 2 years relevant experience. Without PhD: minimum 5 years relevant experience.
- Strong Python and PyTorch; comfortable modifying research codebases and open-source VLA implementations.
- Experience in at least two of: imitation learning, RL, VLA/VLMs, robot learning from demonstration, sim-to-real.
- Track record deploying ML on real robots - not just training policies, but debugging why they fail on actual hardware.
- Working knowledge of ROS2 or equivalent robotics middleware.
- Experience with simulation systems such as NVIDIA Isaac Sim / Isaac Lab.
- GPU inference profiling and optimization (TensorRT, ONNX, CUDA); understand the impact of policy latency on real-time robot control.
Strong Plus- Hands-on with VLA architectures such as c0b0/c0b0.5, OpenVLA, RT-2, Octo, or robotics foundation-model fine-tuning.
- Teleoperation data collection and DAgger / HG-DAgger pipelines.
- World models such as DreamerV3, V-JEPA, or latent dynamics models.
- Experience with contact-rich manipulation, construction, manufacturing, or industrial robotics.
- Publications at CoRL, RSS, ICRA, NeurIPS - valued, but equivalent shipped work on real robots counts.
