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Develops real-time embedded firmware in C/C++ for STM32 and RTOS to process sensor data, control actuators, and integrate radar systems with hardware.
Do you have a passion for golf and a proven track record of leading high-performing sales teams? Would you enjoy leading a team that works with Japan's leading indoor golf facilities, professional golfers, club…
Hands-on technical lead at Emirates Group IT designing and delivering scalable, secure AI computer-vision solutions (multi-camera detection, tracking, Re-ID, 3D vision) with GPU-accelerated inference and real-time video analytics. Core stack: Python/C/Java/NodeJS microservices, PyTorch, OpenCV, ONNX, TensorRT/CUDA, DeepStream, GStreamer, and AWS/Azure/GCP.
Develops plugins, automation tools, and AI-assisted workflows for Autodesk Revit, AutoCAD, and Esri ArcGIS using C#/.NET and Python, connecting BIM, CAD, GIS, databases, and enterprise systems to automate engineering design tasks like drawing generation, asset placement, and validation.
The Applied AI Researcher will research, design, and develop deep learning computer vision solutions, reviewing academic papers and turning novel algorithms into production using PyTorch and Python.
Day-to-day IT support, Google Workspace and Okta administration, and employee on/off-boarding for a construction AI platform.
Leads technical product features for Buildots' construction intelligence platform, defining and implementing analysis engine components while collaborating with algorithms and engineering teams on AI, Computer Vision, and 3D rendering technologies.
Develop end‑to‑end computer vision and deep‑learning solutions for counter‑drone systems, handling detection, classification, tracking, dataset creation, and real‑time model optimization using Python, PyTorch/TensorFlow, C++, and GPU acceleration.
Develop and maintain TripleLift’s high‑throughput Java real‑time bidding platform, building low‑latency distributed services, integrating AI coding assistants, and ensuring reliability across AWS regions.
As a Data Science Engineer intern, you’ll develop Python‑based computer‑vision and generative‑AI models to process construction site images and point‑cloud data, create annotated datasets, automate pipelines, run benchmarks, and produce technical reports.
Axelera AI is hiring a Field Application Engineer to be the go-to technical expert for customers in India, guiding them through evaluation, adoption, and deployment of its Metis AI hardware (PCIe boards, modules, vision gateways) using the Voyager SDK. The role blends hands-on embedded/ML work (Python, C/C++, CUDA, PyTorch/TensorFlow) with customer support, sales enablement, and 30%+ travel.
Build modern data platforms and robust pipelines on Azure cloud, develop and optionally deploy ML models, and work directly with industrial customers to translate data into business value.
Build and fine-tune LLMs, craft AI agents, and deploy ML models using Python, PyTorch/TensorFlow, and LangChain/LlamaIndex in a remote R&D team.
Develops and deploys machine learning applications that analyze operating-room video and sensor data to improve surgical workflows, covering event detection, segmentation, and object detection. Works across the full ML lifecycle in Python with modern ML tooling and cloud infrastructure (ideally AWS) on the Snke platform.
The Staff Machine Learning Engineer builds and fine‑tunes computer‑vision and scene‑understanding models for offline performance measurement of autonomous vehicles, leveraging Python, PyTorch, transformer‑based vision models and large foundation models, and benchmarks their accuracy across platforms and conditions.
Train, debug, and ship computer vision and 3D perception models powering Wayve's ADAS driver-assistance products, working across the full ML lifecycle — building scalable data pipelines (incl. auto-labelling), training, evaluating, and iterating on detection, segmentation, and tracking. Involves both on-car latency-constrained models and offline large-scale data generation using CV/deep learning,
Develops 3D foundation models and world models for autonomous driving systems, focusing on geometric vision, multi-view geometry, and sensor fusion (LiDAR, radar, camera) to enable vehicles to perceive and navigate complex environments.
Build and refine computer vision models to measure autonomous driving performance offline, adapting foundation models for scene understanding and benchmarking accuracy across conditions.
Lead a team building offline scene-understanding models for autonomous vehicles, turning on-vehicle models into robust validation systems that predict counterfactual outcomes and assess AV2.0 driver behavior.
Lead validation of L2+ autonomous driving features, designing test strategies, building metrics, and analyzing results using Python and simulation tools, while collaborating with product, safety, and engineering teams.
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