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Build and scale AI/ML infrastructure for autonomous-driving simulations using large foundation models, focusing on distributed training and ML accelerators.
Build and scale AI/ML infrastructure for ultra-realistic multi-agent simulations using large foundation models to train and test Waymo’s autonomous driving systems.
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 team of data scientists to build and deploy ML models for personalized recommendations and ranking systems that drive conversion and revenue in a large retail ecommerce platform.
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 optimize multilingual AI translation models and MLOps pipelines using AWS SageMaker and containerized workflows to support government mission applications.
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.
Lead the build and operation of MLOps platforms on AWS for autonomous-driving ML workloads, using Ray, Kubernetes, Airflow, and MLflow to ensure reproducible, traceable, and auditable pipelines.
Build and deploy large language models for financial workflows, optimizing training and serving to improve agent efficiency and customer experience in banking systems.
Build and optimize large language models for financial services, focusing on LLM-based methods, training pipelines, and production deployment to improve customer workflows and agent efficiency.
Build and scale deep-learning infrastructure for autonomous-vehicle training, optimizing multi-thousand-GPU clusters, distributed training, and massive video datasets.
Build and optimize distributed GPU training infrastructure for large neural networks, focusing on PyTorch, Megatron-LM, and DeepSpeed to maximize throughput and stability at scale.
Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans…
Design and deploy AI/ML infrastructures and MLOps solutions on Ubuntu, Kubernetes, and cloud platforms for global enterprises, using open-source tools like Kubeflow and MLFlow.
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