Robotics Research Engineer (L4) - Physical AI & Robot Learning

Job highlights

• . 5+ years of relevant experience in robot learning,embodied AI,manipulation,applied ML for robotics,autonomous systems,or a closely related area and 8+ years of overall software / product related work experience
• . You are a strong engineer with experience at the intersection of machine learning and physical robots
• C++ experience is valuable

Robotics Research Engineer (L4) - Physical AI Robot Learning

Hyderabad, India Full-time On-site with real robots

Build Intelligence That Holds Up in the Physical World

A robot can follow a programmed trajectory. The harder challenge is helping it work when the world does not match the plan: a connector is slightly misaligned, a component arrives in a different pose, or parts in a bin are partially hidden from view.

Perceptyne builds industrial humanoid robots for physical work such as assembly, manipulation, and packaging. Making these robots more capable requires advances not only in AI models, but also in perception, control, data collection, evaluation, and the software that brings those pieces together on real hardware.

We are looking for a senior, hands-on Physical AI engineer to help advance that work. You will explore promising approaches in robot learning, adapt and integrate them with Perceptyne s robots, and contribute to the evolution of PR-PhI, our modular robot-intelligence platform. Where it makes sense, you will also help investigate new models tailored to our robots and tasks.

You will report to the founders and begin as a senior individual contributor. You will work alongside our existing AI/ML and robotics engineers, contribute directly to critical code and experiments, and be part of shaping the technical direction as the team grows. This is a role for someone who brings depth and judgment to a collaborative engineering effort-not someone arriving to replace the expertise already here.

What You ll Actually Do

• Your work will span research, software architecture, experiments, and deployment on physical robots. You will collaborate closely with engineers across AI/ML, manipulation, computer vision, controls, embedded systems, and mechanical design.
• Be part of defining priorities and technical approaches for robot learning and Physical AI.
• Evaluate promising robot-learning and physical-intelligence models on Perceptyne hardware. Identify where they work, where they fail, and what it would take to make them useful in practice.
• Adapt, fine-tune, integrate, and deploy selected models for our robot configurations, sensors, action spaces, and manipulation tasks.
• Compare learned approaches against practical alternatives-including classical computer vision, motion planning, visual servoing, force-based control, and task-specific skills-and use the approach that best serves the task.
• Contribute to the architecture and implementation of PR-PhI, connecting perception, robot state, learned policies, planning, skill execution, monitoring, verification, and recovery.
• Set-up new and strengthen our existing data-collection and experimentation workflows, including teleoperation, synchronized sensor and robot-state logging, demonstrations, interventions, dataset quality, versioning, and reproducibility.
• Design meaningful real-robot evaluations that measure task success, robustness to variation, cycle time, intervention needs, latency, and failure modes-not just offline model metrics.
• Build reliable training and inference pipelines, and work through the practical constraints of serving models on robots. Cloud infrastructure may be used where appropriate, with attention to longer-term deployment on local or on-premises compute.
• Work with the broader robotics team to define safe operating boundaries, failure detection, fallbacks, and appropriate human involvement for experimental and deployed capabilities.
• Read and test relevant research quickly, while helping the team distinguish promising results from approaches that are not yet practical for our hardware or customer tasks.
• Write high-quality code, review designs and experiments with peers, mentor engineers through technical collaboration, and help grow the team s collective capability.
• Explore proprietary model development where experimental results and real-world needs make a convincing case for it.

What We re Looking For

You are a strong engineer with experience at the intersection of machine learning and physical robots. You enjoy ambitious rese…