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

Department: Software

Experience: 5+ years

• 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.

• 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. We care more about the depth of your work than an exact year count or previous title.
• Hands-on experience training, fine-tuning, evaluating, or deploying learned behavior on real robots. Simulation and offline research experience are valuable, but are not sufficient on their own for this role.
• Strong Python and modern ML-framework skills, particularly PyTorch or JAX. Ability to write maintainable, performant code; C++ experience is valuable.
• Practical familiarity with relevant approaches such as imitation learning, behavior cloning, diffusion or flow-based policies, vision-language-action models, reinforcement learning, or multimodal perception-action systems.
• Sound robotics fundamentals: coordinate frames, kinematics, calibration, cameras, robot state, action representations, control interfaces, grasping, contact-rich manipulation, latency, and failure recovery.
• Experience with robot-learning data: demonstrations, teleoperation, sensor synchronization, dataset design, quality checks, experiment tracking, and repeatable evaluation.
• Experience with cloud GPU infrastructure, distributed training, model serving, CUDA, TensorRT, ONNX, or edge-compute optimization.
• Strong experimental judgment. You establish baselines, define success criteria, investigate failure modes, and know the difference between a compelling demonstration and a robust capability.
• Systems thinking: you understand how learned models must work alongside perception, planning, controls, safety mechanisms, and real-time robot software.
• Ability to work constructively with experienced engineers across disciplines, share technical judgment clearly, and improve work through code review, mentoring, and collaborative problem-solving.
• Grit, intellectual honesty, and willingness to spend substantial time with physical robots when results do not match expectations.
• Willingness to work full-time on-site in Hyderabad.
• Bachelor’s, Master’s, or PhD in a relevant technical field—or equivalent demonstrated experience.

• Experience with dual-arm manipulation, humanoid robots, dexterous end-effectors, industrial assembly.
• Experience building teleoperation tools, operator interfaces, intervention workflows, or large-scale robot-data pipelines.
• Research publications, open-source contributions, or deployed systems in robotics, embodied AI, computer vision, or controls.
• Experience mentoring engineers or helping establish sound research-engineering practices in a growing team.
• Familiarity with industrial automation, manufacturing workflows, or customer-facing robotics deployments.

• Frontier AI, grounded in real robot and customer constraints.
• Fast experimentation, supported by rigorous evaluation and reproducible engineering.
• Learned intelligence where it helps, and classical perception, planning, control, or deterministic logic where it works better.
• Long-term research ambition, connected to near-term useful capabilities.
• Technical depth, combined with clear communication and practical cross-functional collaboration.