Principal Machine Learning Engineer

5 days ago

San Francisco, California, United States 1Five Full time $200,000 - $250,000 per year

1Five and our clients (seed - publicly traded tech companies) are seeking Principal Machine Learning Engineers with deep expertise in generative AI (diffusion, VLMs, etc.) and/or ML infrastructure, particularly training and inference, and the ability to tech lead teams. Our clients are working on some of the most compelling problems in machine learning today, including:

  • ML to detect and identify rare earth mineral and precious metal deposits using proprietary data sets;
  • Generative AI to automate computer-aided design (CAD);
  • LLMs and GenAI to deliver personalized healthcare to millions of patients;
  • AI & computational biophysics for drug discovery;
  • and more

You Will

  • Lead engineering efforts focused on continuous improvement of the AI platform, focused on rapid build out and iteration on scalable and robust distributed infrastructure for ML training, inference, and evaluation.
  • Support model training and deployment across multiple clusters and multiple clouds, optimizing for throughput and cost.
  • Optimizing efficiency of ML models and other workloads in terms of latency, throughput, memory consumption, etc. (e.g., via GPU performance engineering), pushing the limits of what's possible with the current hardware.
  • Define the long-term vision for the ML platform.
  • Have the opportunity to mentor and guide more junior members of a technical team as well as research interns, fostering an environment of growth and innovation.

You are

  • Strong engineer who constantly strives for technical excellence. You can write clean code and have a deep understanding of the codebases you work in.
  • Deeply experienced with distributed training and inference of large models on GPU clusters and some of the core libraries and frameworks we use: Pytorch, Pytorch Lightning, Pytorch Geometric, and Ray.
  • Independent thinker with a strong sense of ownership and capability of engineering robust systems from first-principles-based conceptualization to state-of-the-art realization.
  • Curious, problem-oriented thinker who is excited to dive deep into the emerging fields of AI + geometry, AI + physics, AI + geology, AI + healthcare... and more

Nice to haves

  • Experienced with building, maintaining and debugging low-level cluster infrastructure running on multiple clouds using Kubernetes and Terraform.
  • Experienced GPU engineer who can quickly figure out performance bottlenecks and architect highly performant code for large scale ML workloads.
  • Experience with XLA, Triton, CUDA, or similar accelerator programming languages and/or deep learning compiler stacks.

Please apply directly if you're interested, thank you

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