Sr. AI Engineer, Physical AI & Robotics

Overview

Own client engagements end to end across Physical AI and robotics, acting as the technical authority on feasibility while hands-on building perception and robotics ML solutions through to delivery.

What you'll do

• Own client engagements end to end, including technical discovery, solution architecture, hands-on build, and delivery, acting as the technical authority on feasibility.
• Translate ambiguous real-world problems into concrete perception and robotics ML architectures; lead demos and proof-of-concept scoping.
• Design and build CV and perception systems, including detection, segmentation, and pose estimation, on the 3D geometry underneath (SE(3), camera models, projection, coordinate frames).
• Own sensor calibration and fusion, including intrinsic/extrinsic, hand-eye, and LiDAR–camera calibration, plus LiDAR processing (registration, ground segmentation, object detection).
• Design egocentric data collection pipelines using wrist/head-mounted cameras, depth sensors, and IMU for robot learning.
• Bridge simulation and deployment via domain randomization, sim setup, and sim-to-real gap analysis.
• Evaluate, select, and integrate VLA models, and fine-tune large pretrained models under data and compute constraints.
• Build supporting services, including agentic loops, REST/gRPC APIs, and PostgreSQL, to expose model inference and robotics services.
• Mentor engineers, drive principled decisions (diagnose before solving), and translate current research into practical client implications.

What you'll need

• Bachelor's degree in Computer Science, Engineering, or related field.

Nice to have

• Agentic systems and prompt engineering, including tool-use agents and multi-step planners.
• API development (REST/gRPC, Docker, basic CI/CD) and PostgreSQL.
• ROS / ROS2 or equivalent robotics middleware.
• C# for Unity-based simulation and tooling.
• Fine-tuning large pretrained models under data/compute constraints.
• Edge/onboard optimization, including TensorRT, ONNX, and quantization.
• RL / RLHF, motion planning, kinematics, or URDF.
• Hands-on experience with robot hardware (Franka, UR, Unitree, or similar).
• Research orientation, tracking arXiv and forming opinions from first principles.
• Publications, open-source work, or notable projects in robot learning or Physical AI.

Details

• Location: Bengaluru, India.
• Work mode: Hybrid, with an in-office presence required.