The role involves building systems for large-scale model training, focusing on distributed training, ML infrastructure, and GPU performance optimization.
We’re hiring Machine Learning Infrastructure Engineers to build the systems that make large-scale model training actually work. This role is for people who enjoy operating at scale—owning distributed training, core ML infrastructure, and fast iteration loops across hundreds of GPUs. If you’ve built or run large training systems in PyTorch or JAX and care about things like sharding, parallelism, and performance, you’ll feel at home here. You’ll work closely with researchers to remove friction, improve reliability, and make it easier to train, evaluate, and deploy models that show up in real systems.
Similar Jobs
AdTech • Digital Media • Marketing Tech • Social Media • Software
The Machine Learning Infrastructure Engineer will design, build, and maintain ML infrastructure to support model experimentation, deployment, and monitoring, enhancing AI capabilities across Later's products.
Top Skills:
AWSBigQueryCi/CdCloudFormationDockerEksFlaskGCPGkeGoGrafanaJavaKubernetesMlflowPrometheusPythonSagemakerScalaTerraform
Artificial Intelligence • Big Data • Information Technology • Software • Analytics
The role involves designing ML pipelines for computer vision, optimizing models for edge deployment, and developing data management systems for sensor datasets.
Top Skills:
C++DeepspeedMlflowOnnxOnnx RuntimeOpenvinoPythonPyTorchPytorch DdpRayRustTensorFlowTensorrtWeights & Biases
Artificial Intelligence • Machine Learning • Software • Cybersecurity
The role involves deploying and optimizing LLMs, designing the ML serving stack, and ensuring high-performance GPU services for production readiness.
Top Skills:
CudaKubernetesLinuxLlmMlNcclPyTorchTensorFlowTensorrtTriton Inference Server
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
