About the role
Senior AI/ML Engineer with Robotics Specialization This role has been designed as ‘’Onsite’ with an expectation that you will primarily work from an HPE office.
Who We Are: Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE. Job Description: Job Description: Senior AI/ML Engineer with Robotics Specialization Location : Singapore (Hybrid Work Model) Role Level: Senior / Senior Specialist (Individual Contributor with technical leadership) Role Summary We are looking for a Senior AI/ML Engineer with Robotics specialization to design and deploy Physical AI solutions—AI models and robotics software that perceive, plan, and act in the real world. You will develop and integrate AI/ML pipelines with ROS/ROS2 systems, and leverage NVIDIA robotics frameworks (e.g., Isaac ROS, Isaac Sim, Omniverse, Jetson, TensorRT) to deliver production-grade capabilities such as perception, sensor fusion, navigation, manipulation, and autonomy. You will work closely with robotics engineers, platform teams, and product stakeholders to move solutions from simulation → real robot deployment, ensuring performance, reliability, and safety. Key
Responsibilities 1) Physical AI / Robotics AI Development Build AI/ML solutions for robotics use cases: perception (2D/3D), localization, mapping, planning, control, manipulation, and autonomy. Develop and fine-tune models for computer vision, 3D point cloud processing, multi-sensor fusion (camera, LiDAR, radar, IMU), and behavior prediction. Apply real-time constraints and optimize for edge deployment. 2) ROS/ROS2 System Integration Develop ROS/ROS2 nodes, packages, and pipelines integrating sensors, ML inference, and robotics stacks. Integrate AI components with ROS ecosystem: Nav2, TF2, rosbag, URDF, RViz, Gazebo/Isaac Sim interfaces, message filters, and lifecycle management. Ensure robust performance under latency, bandwidth, and compute constraints (real robots). 3) NVIDIA Robotics Frameworks (Isaac / Omniverse / Jetson) Build accelerated perception and autonomy pipelines using NVIDIA Isaac ROS, GXF/NITROS, and GPU-accelerated components. Use Isaac Sim / Omniverse to generate synthetic data, validate behaviors, and perform sim-to-real testing. Deploy on NVIDIA hardware (e.g., Jetson, GPU servers) with TensorRT, CUDA, and optimized runtime configurations. 4) Model Deployment, Optimization & Edge Inference Convert and optimize models using ONNX, TensorRT, quantization (FP16/INT8), and performance profiling. Implement real-time inference pipelines, optimize CPU/GPU utilization, and validate throughput/latency targets. Establish reliability practices: health checks, fail-safe modes, and runtime monitoring. 5) Data, Training, Evaluation & Experimentation Design training data strategy: collection, labeling, augmentation, and synthetic data generation. Define metrics and evaluation frameworks: accuracy + robotics KPIs (latency, drift, stability, success rate, collision rate). Manage experiment tracking and reproducibility (e.g., MLflow/W&B equivalent) and model versioning. 6) Engineering Excellence & Collaboration Lead technical design discussions, mentor engineers, and contribute to architecture decisions. Work cross-functionally with hardware, firmware, robotics, cloud, and product teams. Produce clear documentation and guide best practices for production robotics AI. Required
