{"schemaVersion":"jobsearcher.job.v1","id":"2e28126eaecd864fd86c11fe","url":"https://jobsearcher.com/jobs/2e28126eaecd864fd86c11fe","canonicalUrl":"https://jobsearcher.com/jobs/2e28126eaecd864fd86c11fe","title":"Platform Engineer","description":"Where AI Meets Medicine: Build the Future of Drug Discovery in the Heart of Silicon Valley!\r\nMaking medicine that’s never been made means doing what’s never been done. If you’re an engineer, scientist, or builder who thrives on problems no one has solved before, this is your invitation; we want you on the team. We are ready to challenge the status quo and push medicine forward, all in the name of health. Are you up for the challenge? If so, join us!\r\nAbout the Lilly and NVIDIA PartnershipLilly and NVIDIA are launching a new AI co-innovation lab in the heart of Silicon Valley — an up-to-$1 billion, multi-year commitment to solve drug discovery’s toughest challenges. The lab brings Lilly scientists, technologists, chemists and biologists together with NVIDIA engineers under one roof. Together, we are building purpose-built foundation and frontier AI models trained on Lilly data at scale, tightening the feedback loop between automated wet labs and computational dry labs, designing the next generation of medicines for millions of patients across the globe.\r\nWhat You’ll Be DoingAs an ML Ops Engineer, you build and operate the platforms that run the end-to-end machine learning lifecycle. You enable reliable model deployment, operation, monitoring, retraining, and reproducibility at scale. You optimize infrastructure and GPU resources to support research and discovery workloads. You will work closely with engineering and scientific teams to deliver production-ready AI capabilities.\r\nHow You’ll SucceedLead the operational lifecycle of ML models, including deployment, monitoring, and ongoing reliability.\r\nOperate and optimize large-scale inference platforms that support scientific discovery and AI workloads.\r\nEnsure models can be deployed, scaled, monitored, and maintained in production environments.\r\nTest, refine, and improve model accuracy.\r\nWork with data scientists, business analysts and partners to integrate ML models into broader strategies.\r\nAutomate the platform with infrastructure-as-code and CI/CD, and document it well enough that someone else can operate it.\r\nWhat You Should BringStrong Python skills and experience working with machine learning frameworks such as PyTorch, JAX, or TensorFlow.\r\nExperience deploying, operating, and scaling production machine learning platforms, including model serving, monitoring, and large-scale inference workloads.\r\nExperience with MLOps platforms and tools like MLflow, Weights & Biases, KServe, or similar technologies.\r\nProficiency with containerization, orchestration, and distributed compute environments (Docker, Kubernetes, Slurm, Ray).\r\nExperience operating large-scale AI platforms that deploy, host, and optimize machine learning models for production use, using technologies such as Triton, vLLM, or TensorRT-LLM.\r\nExperience with infrastructure automation and CI/CD practices using tools such as Terraform, Ansible, GitHub Actions, or related.\r\nExperience supporting cloud platforms (AWS, Azure, or GCP) and on-premises GPU infrastructure.\r\nKnowledge of observability and operational monitoring, including metrics, logging, tracing, and performance tuning.\r\nAbility to identify and address system, infrastructure, and model performance issues through automation and continuous improvement.\r\nAbility to collaborate effectively with research scientists, AI engineers, and infrastructure teams in a fast-paced environment.\r\nYour Basic QualificationsBachelor’s in Computer Science, Engineering, Statistics, Mathematics, or a related technical field\r\n5+ years of experience in machine learning engineering, ML Ops, or platform engineering.\r\nLocation & Work FlexibilityThis role is based at our Silicon Valley Hub. We offer a flexible hybrid work model, withthree days onsite and two days working remotely each week , supporting both collaboration and work‑life balance.#J-18808-Ljbffr","company":"Eli Lilly and Company","rawCompany":"eli lilly company","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-11-04T02:02:44.841Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Platform Engineer","description":"Where AI Meets Medicine: Build the Future of Drug Discovery in the Heart of Silicon Valley!\r\nMaking medicine that’s never been made means doing what’s never been done. If you’re an engineer, scientist, or builder who thrives on problems no one has solved before, this is your invitation; we want you on the team. We are ready to challenge the status quo and push medicine forward, all in the name of health. Are you up for the challenge? If so, join us!\r\nAbout the Lilly and NVIDIA PartnershipLilly and NVIDIA are launching a new AI co-innovation lab in the heart of Silicon Valley — an up-to-$1 billion, multi-year commitment to solve drug discovery’s toughest challenges. The lab brings Lilly scientists, technologists, chemists and biologists together with NVIDIA engineers under one roof. Together, we are building purpose-built foundation and frontier AI models trained on Lilly data at scale, tightening the feedback loop between automated wet labs and computational dry labs, designing the next generation of medicines for millions of patients across the globe.\r\nWhat You’ll Be DoingAs an ML Ops Engineer, you build and operate the platforms that run the end-to-end machine learning lifecycle. You enable reliable model deployment, operation, monitoring, retraining, and reproducibility at scale. You optimize infrastructure and GPU resources to support research and discovery workloads. You will work closely with engineering and scientific teams to deliver production-ready AI capabilities.\r\nHow You’ll SucceedLead the operational lifecycle of ML models, including deployment, monitoring, and ongoing reliability.\r\nOperate and optimize large-scale inference platforms that support scientific discovery and AI workloads.\r\nEnsure models can be deployed, scaled, monitored, and maintained in production environments.\r\nTest, refine, and improve model accuracy.\r\nWork with data scientists, business analysts and partners to integrate ML models into broader strategies.\r\nAutomate the platform with infrastructure-as-code and CI/CD, and document it well enough that someone else can operate it.\r\nWhat You Should BringStrong Python skills and experience working with machine learning frameworks such as PyTorch, JAX, or TensorFlow.\r\nExperience deploying, operating, and scaling production machine learning platforms, including model serving, monitoring, and large-scale inference workloads.\r\nExperience with MLOps platforms and tools like MLflow, Weights & Biases, KServe, or similar technologies.\r\nProficiency with containerization, orchestration, and distributed compute environments (Docker, Kubernetes, Slurm, Ray).\r\nExperience operating large-scale AI platforms that deploy, host, and optimize machine learning models for production use, using technologies such as Triton, vLLM, or TensorRT-LLM.\r\nExperience with infrastructure automation and CI/CD practices using tools such as Terraform, Ansible, GitHub Actions, or related.\r\nExperience supporting cloud platforms (AWS, Azure, or GCP) and on-premises GPU infrastructure.\r\nKnowledge of observability and operational monitoring, including metrics, logging, tracing, and performance tuning.\r\nAbility to identify and address system, infrastructure, and model performance issues through automation and continuous improvement.\r\nAbility to collaborate effectively with research scientists, AI engineers, and infrastructure teams in a fast-paced environment.\r\nYour Basic QualificationsBachelor’s in Computer Science, Engineering, Statistics, Mathematics, or a related technical field\r\n5+ years of experience in machine learning engineering, ML Ops, or platform engineering.\r\nLocation & Work FlexibilityThis role is based at our Silicon Valley Hub. We offer a flexible hybrid work model, withthree days onsite and two days working remotely each week , supporting both collaboration and work‑life balance.#J-18808-Ljbffr","datePosted":"2026-11-04T02:02:44.841Z","dateModified":"2026-11-04T02:02:44.841Z","hiringOrganization":{"@type":"Organization","name":"Eli Lilly and Company","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"2e28126eaecd864fd86c11fe"},"url":"https://jobsearcher.com/jobs/2e28126eaecd864fd86c11fe"}}