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Builds and maintains data infrastructure for an AI biomedical research platform, focusing on ingesting, harmonizing, and serving multi-modal neurodegeneration datasets for AI tools like InsightEngine and OpenScientist.
Lead the development of advanced AI/ML systems powering next-generation journalism at The Washington Post, focusing on AI-powered search, retrieval, ranking, content understanding, and generative AI reader experiences using Python, PyTorch/TensorFlow/JAX, and cloud infrastructure.
Build and deploy ML models for personalization, recommendations, and ranking at The Washington Post, using Python, PyTorch/TensorFlow/JAX, and large-scale behavioral data to power intelligent reader discovery experiences.
We're looking for a Data Science Consultant – Life Sciences to join our team in London, UK in a hybrid working mode. In this role, you will help leading pharmaceutical and life sciences organizations leverage data,…
Working at Moffitt is both a career and a mission: to contribute to the prevention and cure of cancer. As the only National Cancer Institute-designated Comprehensive Cancer Center based in Florida, Moffitt employs some…
Overview Medallia is the pioneer and market leader in Experience Management. Our award-winning SaaS platform, Medallia Experience Cloud, leads the market in the management of experiences, insights, and actions for…
Job Description An employer in the Dayton, Ohio, area is seeking a TS/SCI AI/ML Engineer for a contract-to-hire opportunity. This individual will be responsible for designing, developing, and deploying advanced…
Develops next-gen AI/ML GenAI solutions on Google Cloud, focusing on large-scale system design, model deployment, and GenAI techniques like LLMs and multi-modal models to enhance Google’s products and services.
Lead a full-stack engineering team for Google Cloud, setting priorities, managing performance, and driving technical vision while handling system design and code reviews.
Design scalable AI platforms and productionise ML/NLP models into cloud-native services using Python, AWS/Azure, Docker, and Kubernetes.
Remote AI Engineer leading end-to-end AI/ML projects with Python, deep learning, NLP/LLMs, and MLOps, requiring active IRS MBI clearance.
Builds and maintains AI-driven analytics dashboards in Tableau and Looker Studio to track support and cloud operations metrics, using SQL and ML to uncover trends and automate reporting workflows.
Lead development of next-generation data infrastructure for AI agents, architecting heterogeneous compute pooling, vector storage, and high-performance caching across CPUs, GPUs, and NPUs. Design scalable platforms for multimodal data processing, indexing, and intelligent management, integrating NLP, vision, and time-series capabilities.
Leads the design and development of scalable infrastructure for Google’s next-gen server platforms, focusing on AI modeling, hardware simulation, and system reliability. Works with cross-functional teams to address technical challenges in hardware modeling, security, and deployment at massive scale.
A generalist data scientist applies ML to solve data problems (e.g., quality, classification, embeddings) and builds tools for analysts. Works independently on projects, embedded with product/data teams, and partners with engineers, analysts, and PMs.
Builds and scales AI-powered backend services and platforms for Apple’s internal systems, focusing on data pipelines, generative AI integration, and intelligent automation to improve operational efficiency and user experiences.
Leads a team of engineers managing Google’s Slicer infrastructure, optimizing sharding/load-balancing for 15B+ requests/sec across Google’s core products (e.g., Cloud, Search). Oversees technical strategy, mentors engineers, and collaborates with product teams to enhance distributed system performance.
Lead the design and deployment of AI/ML systems for cybersecurity, focusing on LLMs, RAG pipelines, and anomaly detection to analyze security telemetry and logs.
Leads technical integration of AI/ML features across 25+ languages and 40+ countries, managing data pipelines, model evaluation, and hardware/software integration for global Apple Intelligence and hardware launches.
Build and enhance the machine learning platform backend services to manage model lifecycles and perform NLP analysis using Python, Java, Kubernetes, and cloud technologies.
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