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Build and lead AI/ML systems for energy infrastructure, including predictive maintenance, anomaly detection, and time-series forecasting on industrial sensor data.
Design and implement storage solutions for AI/ML workloads on Google Cloud Platform, including benchmarks, performance tuning, and cross-team collaboration.
Build and harden ML models that detect prompt-injection and other AI security threats across languages and data types, using NLP and vision techniques in production.
Design and deploy self-running AI systems and predictive models using Azure ML workflows for client projects.
Build generative and predictive ML models to decode cellular behavior and guide drug discovery using single-cell multi-omics and perturbation data.
Build C++/Python frameworks and APIs to run Vision and Generative AI models efficiently on custom AI accelerators, optimizing performance and integrating with compiler/runtime teams.
Build and deploy scalable ML infrastructure, owning end-to-end systems from data pipelines to production models while optimizing for performance and cost.
Design and automate scalable SageMaker environments for ML model deployments across Europe using AWS, Python, and CI/CD pipelines.
Build and maintain AI/ML infrastructure to deploy models into live game engines, enabling scalable, real-time gaming experiences for a cross-studio team.
Build and deploy NLP models and LLMs to turn messy, multi-modal communication data into structured compliance signals at scale.
Designs and builds generative AI systems using LLMs, agentic architectures, and RAG pipelines, then deploys and monitors them in production.
Build and scale generative and predictive ML models for cellular behavior using PyTorch and distributed training, bridging research prototypes to production-grade systems in a TechBio company.
Build next-gen generative models of cellular behavior to guide drug discovery, using single-cell multi-omics and ML to predict intervention effects and design experiments.
Build and scale an LLM platform, CI/CD pipelines, and Kubernetes-based infrastructure for ML services, ensuring reliability and real-time model serving.
Build and scale ML services for text extraction, search, ranking, and recommendations that power Apple News and Books for millions of users.
Build offline perception models for autonomous trucks, using deep learning and sensor fusion to auto-label data for training and simulation.
Build vision-language-action and reinforcement learning models for real-world robotic systems, training policies that generalize across hardware and deployments.
Build and maintain scalable ML systems for enterprise commerce clients, focusing on reliability, low-latency inference, and MLOps best practices using Python, PyTorch, AWS, and Kubernetes.
Build and deploy 4D Vision™ systems for industrial robots, extending object detection, depth estimation, and pose estimation using PyTorch and foundation models to enable precise automation on factory floors.
Build and deploy ML systems for crypto fraud detection, recommendations, and chatbots using deep learning and graph neural networks to secure and improve Coinbase’s platform.
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