Senior AI Engineer

About Team :

The Generative AI team collaborates closely with clients to understand their unique needs and to customize AI solutions that enhance their operational efficiency and product offerings. Our team of solution architects and consultants is at the forefront of AI technology, advising clients on best practices and innovative approaches to integrate AI seamlessly into their workflows.

Role Summary

Were hiring a Senior AI Engineer to own client engagements end to end across Physical AI and robotics — from understanding a clients real-world environment and constraints, to designing the perception and robotics ML solution, to building it hands-on through to delivery.

This is a solutions-lead role that codes. You drive the engagement from problem to production and are a strong enough perception engineer to be trusted in front of clients and to write the code that ships. The technical spine is computer vision, perception, and 3D geometry; the surrounding robotics stack — LiDAR, calibration, sim-to-real, VLA models — is where you apply that core to real deployments. You own the can we actually do this answer, and then you go build it. You are research-aware, principled in how you diagnose problems, and comfortable operating where best practices are still being defined.

Key Responsibilities

• Own client engagements end to end — 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 — detection, segmentation, pose estimation — on the 3D geometry underneath (SE(3), camera models, projection, coordinate frames).
• Own sensor calibration and fusion — intrinsic/extrinsic, hand-eye, and LiDAR–camera — 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 randomisation, 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 — 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.

Required Qualifications

You do not need every skill below — we hire for a strong core and functional familiarity with the rest

• 4+ years in robotics ML, computer vision, or technical solutions engineering.
• Strong CV and perception — detection, segmentation, pose estimation, optical flow.
• Solid 3D geometry — SE(3) transformations, camera models, projection, coordinate frames.
• Sensor calibration — intrinsic/extrinsic, hand-eye, and multi-modal alignment.
• LiDAR processing and sensor fusion, including LiDAR–camera calibration.
• Sim-to-real experience — domain randomisation and sim environments (Isaac Sim, MuJoCo, Gazebo, or Unity Robotics Hub).
• Conceptual grasp of VLA architectures and the current SOTA landscape.
• Strong Python (PyTorch); able to read and implement research papers.
• Prior client-facing or presales technical experience.
• Clear communicator across technical and non-technical audiences.

Preferred Qualifications

• Agentic systems and prompt engineering — 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 optimisation — TensorRT, ONNX, quantisation.
• RL / RLHF, motion planning, kinematics, or URDF.
• Hands-on with robot hardware (Franka, UR, Unitree, or similar).
• Research orientation — tracks arXiv, forms opinions from first principles.
• Publications, open-source work, or notable projects in robot learning or Physical AI.

Technical Stack

• Languages: Python (PyTorch)
• Perception / 3D: OpenCV, Open3D, PCL, point cloud processing
• Simulation: Isaac Sim, MuJoCo, Gazebo, Unity Robotics Hub
• Robotics Middleware: ROS / ROS2
• Models: VLA models (RT-2, OpenVLA, π₀, Octo), ViTs, multimodal, hosted and open-source
• Deployment: REST/gRPC, Docker, PostgreSQL; TensorRT / ONNX for edge
• Core Areas: CV and perception, 3D geometry, calibration, LiDAR, sensor fusion, sim-to-real, agentic systems

Success Measures

• Client engagements delivered with clear architecture, working perception/robotics systems, and evaluation coverage.
• Reusable solution patterns and accelerators created from recurring engagement needs.
• Robust calibration, data collection, and sim-to-real workflows that transfer reliably to real hardware.
• Sound VLA and model-selection/fine-tuning decisions, grounded in the current SOTA landscape.
• Effective mentorship and principled technical guidance across the Technical Solutions team.