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Designs and advises on scalable AI/ML cloud solutions for enterprise customers, focusing on distributed training and production deployments using PyTorch, Kubernetes, and related tooling.
Research and build large-scale machine learning models for physical AI systems, focusing on robotics, reinforcement learning, and multimodal models to enable intelligent agents to interact with the real world.
Design and deploy scalable AI/ML solutions for customers, advising on distributed training and production-scale deployments while collaborating with engineering teams.
Builds and optimizes large-scale AI infrastructure, including Kubernetes clusters, RDMA networking, and GPU orchestration to improve efficiency and scalability of AI training/inference systems.
Build and lead the automation infrastructure that evaluates NVIDIA’s AI and accelerated-computing workloads, turning complex benchmarks into scalable, reproducible workflows.
Lead a data-science team to build and deploy ML models that power personalized shopping experiences and recommendation systems for an ecommerce retailer.
Lead the architecture and delivery of ML and Generative AI platforms for home lending, turning prototypes into production systems that scale securely and improve customer outcomes.
About Inflection AI Inflection AI is a Public Benefit Corporation empowering people with human-centered, emotionally intelligent AI. We’re shaping the future of AI by combining emotional intelligence (EQ) and raw…
We are an applied AI lab building end-to-end software agents. We're the makers of Devin, the first AI software engineer. Our team is extremely talent-dense. Among our founding team, we have world-class competitive…
Build and deploy GenAI/ML models end-to-end on GCP, containerize with GKE, and implement MLOps pipelines for scalable AI solutions in a regulated pharma context.
Builds and optimizes 3D perception models that fuse camera, LiDAR, and radar inputs for autonomous trucks using PyTorch/TensorFlow.
Build and optimize multilingual AI translation models and MLOps pipelines using AWS SageMaker and containerized workflows to support government mission applications.
Build and operate a unified GPU compute platform for AI training and inference, hiding cloud complexity behind Kubernetes operators and schedulers that manage multi-cloud NVIDIA clusters.
Leads the design and evolution of Target’s enterprise AI/ML platform, defining architecture for scalable, cloud-native ML lifecycle management, deployment, governance, and observability to enable cross-functional teams to build and deploy AI solutions at scale.
Optimize and port AI inference/training kernels (SGLang, Miles) across NVIDIA/AMD GPUs, TPUs, CPUs, and emerging accelerators to maximize performance on heterogeneous hardware.
Build and deploy large-scale AI models (LLMs, diffusion, GNNs) for biomedical discovery, owning end-to-end training pipelines and distributed systems.
Research and build power-aware AI infrastructure systems that scale data centers sustainably using distributed computing and optimization techniques.
Lead the MLOps platform for autonomous-driving teams, building scalable AWS/Kubernetes pipelines with Ray, Airflow, and MLflow to train and deploy perception models in safety-critical automotive systems.
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