Robot Learning Engineer

Origin; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Origin is seeking a Robotics Engineer in Bengaluru, India. The position entails owning the complete lifecycle of learned components on the OG-1 robot, from data collection to deployment. Candidates should have strong expertise in Python and PyTorch, and experience shipping systems on physical robots. A BS/MS/PhD in CS, Robotics, or ML is required along with significant experience in robotics and machine learning. This is a hands-on role that demands strong problem-solving and optimization skills, ideally suited for those with a background working in robotics or AI systems.

Qualifications

• 3-4 years of hands-on experience in robotics, machine learning, or applied AI systems.
• Experience shipping learned systems on physical robots.
• Track record deploying ML on real hardware.

Responsibilities

• Train and deploy VLA models for contact-rich manipulation.
• Build the data flywheel using teleoperation pipelines.
• Optimize models for edge deployment.

Skills

Strong Python

PyTorch

Imitation learning

Reinforcement learning (RL)

Vision-language models

Robot learning from demonstration

Sim-to-real

Education

BS/MS/PhD in CS, Robotics, ML

Tools

ROS2

Isaac Sim

TensorRT

ONNX

CUDA

Job description

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.