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About the role: We are looking for a Technical Product Marketing Manager to shape how developers understand and adopt Mojo and MAX. You’ll own release messaging, working with engineering and product to decide what to…
Job Purpose The AI & BDA Engineer is responsible for the end-to-end development and implementation of AI and Big Data solutions, including data processing, analytics, machine learning, Generative AI, Large Language…
We’re looking for a Senior Azure Platform / AI Engineer for a long-term engagement with an enterprise client. You’ll work directly with the client’s IT and Security teams in a sprint-based model. What you’ll do: Design…
Overview Do you want to be at the forefront of innovating the latest hardware and systems designs to propel Microsoft’s cloud growth? Are you seeking a unique career opportunity that combines technical capabilities,…
🚀 Welcome to Your Next Adventure! We are looking for an Engineering Lead to own and drive Snoonu's Conversational AI and Driver Automation platform — a portfolio spanning IVR systems, multi-channel chatbots, and a…
About Sarvam Sarvam is building the bedrock of Sovereign AI for India. The company is developing India’s full-stack sovereign AI platform, building across research, models, infrastructure and applications with a…
About Huawei Research and Development UK Limited Founded in 1987, Huawei is a leading global provider of information and communications technology (ICT) infrastructure and smart devices. We have 207,000 employees and…
Наша CV команда создает лучший в Сбере продукт для автоматического распознавания документов, используя самые передовые технологии. Главными задачами для нас являются разработка и улучшение OCR-модели на базе VLM, сбор…
About Us: ClearRoute is an engineering consultancy bridging Quality Engineering, Cloud Platforms and Developer Experience. We help enterprises reliably bring high-impact digital products to market faster, cheaper, and…
Builds and maintains ML infrastructure (training, serving, CI/CD, observability) for AI/ML models and LLMs, bridging research and production teams to deploy scalable, reliable systems.
SRE role at SberTech keeping an enterprise AI platform (ChatGPT-style chat with RAG on Qdrant) reliable in production: owning SLOs, on-call and postmortems, running Kubernetes, CI/CD and IaC, building observability with Prometheus/Grafana and OpenSearch, and tuning PostgreSQL, Kafka, Redis and S3. Hybrid work in Moscow.
ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible…
We are building a new live translation product We are looking for an ML Engineer to build the real-time speech translation backend that powers it You will own the real-time speech translation backend end-to-end, from…
The Applied AI Engineer will build and deploy end-to-end AI features by integrating machine learning models into production systems. The role involves optimizing model performance, designing agent workflows, and ensuring reliability using technologies like Python, PyTorch, JAX, and LLMs.
NVIDIA is hiring a Senior Solution Architect in Budapest to design and scale distributed AI/MLOps infrastructure on-prem or in the cloud, integrating NVIDIA technology into HPC and customer environments via POCs/POVs. Core stack: Kubernetes, containers, MLOps tooling, PyTorch, vLLM, and TritonServer.
At Digital Turbine, we make mobile advertising experiences more meaningful and rewarding for users, app publishers, and advertisers — intelligently connecting people in more ways, across more devices. We provide app…
Cohu, Inc. is building an AI group within Semiconductor Testing to solve and deploy solutions to hard, real-world problems that we encounter. This role will take on problems that do not have clean answers yet, build…
Designs, deploys, and champions generative-AI systems while training teams to adopt AI responsibly using Python, LLMs, RAG, and agentic AI.
Build and optimize ML infrastructure powering self-improving AI systems across distributed training/RL, inference serving, and GPU kernels. Day-to-day work involves GPU scheduling, checkpointing, KV-cache and batching optimization, and CUDA/Triton kernel development alongside researchers.
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