Robot Learning Engineer
At a glance
Mid-level AI Research role at Origin. Bengaluru · full-time.
Pay not stated AI Research salary
aimlroles summary, based on the employer's posting.
What you'll do
- Train vision-language-action models for contact-heavy robot work, then carry them through deployment
- Build teleoperation and online-correction workflows, and curate the demonstrations used for training
- Prototype world models for surface conditions, spray behavior and anomaly detection
- Create offline tests that forecast finishing quality, then optimize models for Jetson deployment
- Connect learned action proposals to deterministic safety checks that enforce operating limits
What you bring
- A CS, robotics or machine-learning degree, or equivalent experience shipping learned systems on physical robots
- Strong Python and PyTorch skills, plus experience deploying and debugging ML on real robot hardware
- Experience in at least two listed areas, such as imitation learning, reinforcement learning or vision-language models
- Working knowledge of ROS2 or comparable robotics middleware, and experience with simulation systems such as Isaac Sim
Who this fits
This suits a hands-on engineer who takes robot-learning work from data and training through real-world deployment. The role is onsite in Bengaluru and calls for roughly three to four years of practical experience in robotics, machine learning or applied AI systems.
From the employer
About Origin
Origin is building physical AI for the built world. Our robots autonomously finish building interiors at production quality. OG-1 is deployed on live NYC commercial construction sites today. Backed by Accel.
Our 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.
The Role
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 Do
- Train and deploy VLA models for contact-rich manipulation using our imitation learning infrastructure.
- Build the data flywheel: teleoperation pipelines (GELLO, SpaceMouse, VR), DAgger-style online correction, demonstration curation.
- Research and prototype world models for surface state prediction, spray dynamics, and anomaly detection.
- Design offline evaluation metrics that predict real-world finishing quality before deployment.
- Optimize models for edge: TensorRT compilation, latency profiling, memory budgeting on dual Jetson AGX Orin.
- Hands-on experience with world models, including building or working with predictive or generative environment models (e.g., latent dynamics, video prediction, or planning-oriented models)
- Design the interface where learned policies propose actions and deterministic safety layers enforce constraints.
Requirements
- BS/MS/PhD in CS, Robotics, ML, or equivalent experience shipping learned systems on physical robots.
- Strong Python and PyTorch; comfort modifying research codebases (you'll work directly with open-source VLA implementations).
- Experience in at least two of: imitation learning, RL, vision-language models, robot learning from demonstration, sim-to-real.
- Track record deploying ML on real hardware: not just training to convergence, but debugging why the policy fails on the actual robot.
- Working knowledge of ROS2 or equivalent robotics middleware.
- Experience working with Simulation Systems like Isaac Sim.
- GPU profiling and optimization (TensorRT, ONNX, CUDA); you understand why 200ms policy latency kills contact control.
Strong Plus
- Hands-on with VLA architectures (π0/π0.5, OpenVLA, RT-2, Octo) or foundation model fine-tuning for robotics.
- Teleoperation data collection and DAgger/HG-DAgger pipelines.
- World model architectures (DreamerV3, V-JEPA, latent dynamics models).
- Construction, manufacturing, or contact-rich industrial domains.
- Publications at CoRL, RSS, ICRA, NeurIPS: valued but equivalent shipped work counts.
Note - We’re ideally looking for candidates with 3–4 years of hands-on experience in robotics, machine learning, or applied AI systems.