· 5+ years of software engineering experience, with 3+ years specialized in deploying large-scale video analytics or telemetry pipelines integrated with operational monitoring systems (C2, VMS, or Smart City platforms).
Join a high-impact engineering team building robust computer vision capabilities for advanced robotic systems used in real-world production environments.
Design and implement advanced computer vision algorithms for 3D reconstruction, image processing, object detection, recognition, tracking, and scene understanding to enhance the capabilities of our robotic systems.
TDKSensEI is seeking a high-performing Computer Vision Sales that combines technical expertise in AI/imaging processing with sales strategy to drive revenue. Also as a dual role, the candidate is also required to seek AI predictive maintenance opportunities within the industrial sectors.
Build and maintain production-grade embedded software for on-device computer vision inference (camera ingest, preprocessing, inference, postprocessing, telemetry) primarily in C++, with Rust as an option where appropriate.
C2-Scale Analytics Pipelines: Architect robust Python/C++ pipelines using frameworks like NVIDIA DeepStream, GStreamer, or OpenCV to execute concurrent video analytics across hundreds of live camera feeds.
Liaise with a cross-functional team to research and develop algorithms for image processing and computer vision in automated optical inspection systems.
Liaise with a cross-functional team to research and develop algorithms for image processing and computer vision in automated optical inspection systems.
Serve as the primary technical point of contact for customers, establishing strong technical relationships and ensuring their success with CoreWeave's cloud infrastructure offerings, focusing on bare-metal compute infrastructure and end-to-end cluster delivery within high-performance compute (HPC) environments.
Liaise with a cross-functional team to research and develop algorithms for image processing and computer vision in automated optical inspection systems.
Model Training & Fine-Tuning: Train, evaluate, and improve deep learning models across a variety of tasks, including classification, object detection, segmentation, and action recognition.
Lead strategic AI initiatives: Drive the end-to-end design, training, and deployment of state-of-the-art deep-learning and multimodal models that scale to millions of users — spanning computer vision, vision-LLM, multimodal fusion, and AI-agent applications.
Train, fine-tune, and evaluate computer vision models for tasks such as classification, object detection, segmentation, pose estimation, and action recognition.
C2-Scale Analytics Pipelines: Architect robust Python/C++ pipelines using frameworks like NVIDIA DeepStream, GStreamer, or OpenCV to execute concurrent video analytics across hundreds of live camera feeds.