your tasks Develop and deploy vision-based perception algorithms for autonomous inspection robots using deep learning and machine vision techniques, including CNN- and ViT-based architectures. Design and implement supervised, self-supervised, and unsupervised learning methods for multi-modal data (RGB, LiDAR, thermal, etc.). Build and optimize models for: Object detection and tracking (2D & 3D) Anomaly detection LiDAR-based semantic segmentation and 3D point cloud understanding Optimize models for on-device inference (quantization, pruning, distillation) and deploy them on edge hardware (NVIDIA Jetson Xavier/Orin/Thor, etc.). Integrate perception pipelines into robotic systems via ROS1/ROS2 and collaborate closely with software and robotics teams. Support MLOps workflows, ensuring reproducible training, evaluation, and CI/CD for AI models. Support and maintain cloud-based AI pipelines on AWS (SageMaker, Bedrock, Lambda, S3) for scalable training, inference, and model lifecycle management. Contribute to research on multi-modal and 3D representations (Gaussian Splatting, NeRF, OpenCLIP-based embeddings). Participate in continuous improvement of our AI infrastructure for mission planning, semantic mapping, and robot autonomy. your profile Degree in Computer Science, Robotics, or a related technical field. 2+ years of professional experience in computer vision, deep learning, or robot perception. Strong understanding of Vision Transformers (ViTs), Convolutional Neural Networks (CNNs), and DETR-based models for object detection. Hands on experience with Frameworks including PyTorch, OpenCV, MMDetection, and/or Detectron2. Applied experience in Optimization & Deployment with TensorRT, ONNX, Docker, and/or NVIDIA DeepStream. Direct experience with Data Annotation using CVAT, and/or Label Studio, and/or 3D Point Labeler. Practical experience in Versioning & MLOps using GitLab CI/CD, MLflow, n8n, AWS SageMaker, and/or Bedrock. Strong programming skills in Python, C++, Bash. Strong analytical, problem-solving, and growth mindset. Excellent collaboration between cross-functional AI and robotics teams. Passion for innovation, autonomy, and pushing the boundaries of perception systems. Ability to work independently and manage multiple priorities in a fast-paced environment. Fluent in English. helpful additional qualifications Experience with LLMs, VLMs, VLAs and multi-modal embeddings. Familiarity with semantic scene graphs-based 3D Gaussian splatting. Exposure to cloud-based data services or AI workflow orchestration. Contributions to open-source projects in AI/robotics or publications in the field. Originally posted on Himalayas