{"schemaVersion":"jobsearcher.job.v1","id":"96ea2cddd0ce7228ec25efaa","url":"https://jobsearcher.com/jobs/96ea2cddd0ce7228ec25efaa","canonicalUrl":"https://jobsearcher.com/jobs/96ea2cddd0ce7228ec25efaa","title":"Machine Learning Engineer","description":"I'm working with a rapidly growing, well-funded AI company building some of the most technically ambitious real-world AI systems today.They're looking for an ML Infrastructure Engineer to join a highly technical team responsible for building the infrastructure that powers large-scale model training, experimentation, and deployment.This is a hands-on engineering role for someone who enjoys solving difficult systems problems at the intersection of machine learning, distributed systems, and infrastructure.What you'll work on:Build and scale infrastructure for training large machine learning modelsDevelop distributed training systems and improve training efficiency, reliability, and throughputBuild data pipelines and infrastructure supporting large-scale ML workloadsImprove GPU utilization, compute orchestration, checkpointing, and experiment managementDevelop tooling that enables researchers and ML engineers to iterate fasterDiagnose performance bottlenecks across training, data, and compute systemsHelp take ML systems from experimentation through production and real-world deploymentWhat they're looking for:Strong Python and software engineering fundamentalsExperience building ML infrastructure, training infrastructure, or large-scale distributed systemsExperience working with PyTorch and modern ML training stacksStrong understanding of distributed computing and GPU-based workloadsExperience building reliable systems that support ML research or production MLComfortable working in a fast-moving environment with significant technical ownershipEspecially interesting backgrounds include:Distributed training and large-scale model trainingML platforms / internal training infrastructureGPU infrastructure and compute orchestrationLarge-scale data infrastructure and pipelinesPerformance optimization for ML workloadsInfrastructure supporting robotics, embodied AI, computer vision, or other real-world ML systemsExperience taking ML systems beyond research and into production or physical-world environmentsThis is a great opportunity for an engineer who wants to work on hard ML systems problems at scale while being much closer to the models and real-world applications than you would be on a traditional infrastructure team.Bay Area - 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