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Designs and deploys advanced AI/ML systems (LLMs, agentic workflows, multimodal models) for government/commercial clients, optimizing for production, edge, and security.
Design and maintain MLOps infrastructure and pipelines to streamline ML model development, training, and deployment for AI-powered developer tools.
Research Engineer building and training large language models from scratch for coding tasks, deploying them into production to power JetBrains' AI platform.
Разработка и внедрение LLM-агентов для преобразования истории операций в интерактивную, натурально-языковую аналитику с автоматическими тегами и персонализированными инсайтами. Используются GigaChat, Finetuning, RAG-системы и классическое ML для анализа транзакций.
Build and deploy AI models that monitor vehicle health in real time using logs, traces, and sensor data, then optimize them to run on resource-constrained edge devices.
Develops real-time perception and prediction systems for autonomous delivery robots, focusing on multi-object tracking, open-set world understanding, and motion forecasting using multi-sensor data (LiDAR, camera, radar). Translates research into production-grade embedded solutions with safety guarantees, ensuring robust performance in complex urban environments.
Helsing is seeking an AI Research Engineer to develop machine learning models for electronic warfare systems, focusing on RF signal processing and deployment on embedded hardware. The role involves end-to-end research and implementation, working alongside hardware and RF engineers to solve complex problems in contested environments.
Optimizes AI inference performance at scale for DeepMind’s general-purpose learning agents, analyzing bottlenecks across application, model, and distributed infrastructure to maximize hardware throughput and reduce latency.
Principal Data Scientist builds and scales AI/ML systems for media metadata and discovery, focusing on computer vision, generative models, and multimodal architectures.
The Senior Forward Deployed Solution Engineer will work directly with customers to design, deploy, and troubleshoot complex infrastructure architectures involving Kubernetes, AI/GPU systems, and cloud-native environments. This hands-on role requires deep technical expertise in automation and systems engineering to ensure successful customer outcomes and production readiness.
Designs and leads SoC software architecture for automotive driver-facing systems, including ADAS alerts, controls, and driver-monitoring tech, focusing on latency, reliability, and edge AI integration.
Designs and optimizes GPU-powered infrastructure for GenAI/LLM workloads, focusing on distributed training, performance tuning, and Kubernetes/OpenShift deployments.
The Senior Software Engineer II will design, develop, and deploy scalable AI-powered solutions by integrating machine learning models into production environments. The role focuses on MLOps, API development, and optimizing AI workflows using technologies like Python, Docker, Kubernetes, and various cloud AI services.
DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly…
DRW est une société de négoce diversifiée avec plus de 3 décennies d'expérience qui réunit une technologie sophistiquée et des personnes exceptionnelles pour opérer sur les marchés du monde entier. Nous valorisons…
This role involves developing high-quality C++ embedded software for automotive AI-driven systems, such as occupant monitoring. The engineer will collaborate with cross-functional teams to integrate and optimize deep learning models for deployment on resource-constrained automotive hardware.
Develops 3D computer vision and spatial AI systems for wearable AR tech in construction, focusing on scene understanding, BIM integration, and embedded deployment to enable real-time object recognition and quality assurance on-site.
The AI Platform & Machine Learning Engineer will build and maintain scalable infrastructure for training, deploying, and monitoring machine learning models for wearable construction technology. The role focuses on the end-to-end ML lifecycle, utilizing tools like Python, PyTorch, and containerization to bridge the gap between research and production.
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