Machine Learning Engineer @ Apiphany
Apiphany
San Francisco, CAOn-siteFull-time$140k – $220k / year
About this role
Machine Learning Engineer @ Apiphany
!Apiphany(https://app.ashbyhq.com/api/images/org-theme-wordmark/c5b56725-fa4b-491e-aec1-4528dc005272/8e6367bf-744c-4018-87ce-9057ad347017/4d3afa5c-6994-4759-b04e-3e8a3f48da7b.png)(https://www.apiphany.ai/)
(https://jobs.ashbyhq.com/apiphany)
Machine Learning Engineer
Location
San Francisco
Employment Type
Full time
Location Type
Hybrid
Department
Engineering
About Apiphany
Apiphany is a pioneering foundational AI company for physical product development. We empower global innovators in automotive, aerospace, medtech, and energy to transform mountains of unstructured technical data into real-time, actionable insights. Backed by world-class investors from Markforged, Databricks, GM, and Character, our mission is to revolutionize how engineering decisions are made, turning complexity into clarity for the world’s top manufacturers.
Our models are built for the complexities of engineering and manufacturing. Our models understand physics principles, design specifications, and program constraints. We’re a small, elite team of builders from Stanford, Berkeley, MIT, UW, and CMU, alongside industry leaders from GM, Ford, and Genesis Therapeutics. We’re passionate about transforming hard-tech and building a category-defining company together.
About the Role
As a ML Engineer at Apiphany, you’ll develop and implement advanced machine learning models to tackle some of the hardest problems in the physical world. You’ll design systems that can reason about complex engineering data and build AI that understands physics, design constraints, and real-world performance tradeoffs.
This is a role for builders who want to push the frontier of what AI can do in the physical world.
Experience & Skills
Expert-level programming skills in Python
Solid understanding of deep learning and its applications in natural language processing (NLP)
Deep understanding of large language models (LLMs)
Exceptional
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