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Build and maintain ML infrastructure, data pipelines, and model serving systems to enable faster, reliable AI experimentation and deployment.
Build and operate ML infrastructure, design training and inference systems, and optimize model serving for performance and scalability.
Build and maintain ML infrastructure, pipelines, and serving systems to enable scalable model training, evaluation, and inference with observability and reliability.
Build and operate ML infrastructure, design training/evaluation/deployment systems, and optimize model serving for performance and cost.
Build and maintain ML infrastructure, including model serving, training pipelines, and observability, to support scalable, low-latency AI systems.
Build and operate ML infrastructure, design evaluation systems, and optimize AI pipelines for training, deployment, and inference.
Build and maintain ML infrastructure, model serving systems, and data pipelines to deploy and optimize AI models in production.
Build and maintain ML infrastructure, deploy models, and optimize distributed systems for performance, scalability, and cost efficiency.
Build and maintain ML infrastructure, pipelines, and tooling for training, evaluation, and production deployment of AI models.
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 AI-driven features using LLMs, RAG, and Agentic AI to enhance Workday’s HR/finance products, deploying scalable ML models and APIs with Python.
Build and deploy ML models for Apple News, Books, and Stocks to power recommendations, search, and AI features using Python, PyTorch, and TensorFlow.
Build vision-language-action and reinforcement learning models for real-world robotic systems, training policies that generalize across hardware and deployments.
Build and deploy scalable AI/ML systems, LLMs, and generative AI services for Amazon’s Customer Service, including pipelines, model serving, and governance.
Build, deploy, and maintain AI/ML models including LLMs and agentic systems to power scalable products and workflows.
Lead a team to build and deploy ML, ML Ops, and Generative AI systems that power AI-driven products and platforms at scale.
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.
Research Scientist driving novel AI/ML and computer vision methods, publishing at top conferences, and collaborating with engineering to scale solutions for a large userbase.
Lead AI/ML product development for an insurance-focused platform, owning ML features that automate renewals and drive data-driven insights for brokerages.
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