Apiphany
Machine Learning Engineer
About this role
Apiphany is seeking a Machine Learning Engineer to develop advanced AI models that transform unstructured engineering data into actionable insights for automotive, aerospace, and manufacturing industries. You'll build systems that reason about complex physical problems, combining deep learning with physics-based constraints to revolutionize how engineering decisions are made.
What you'll do
- Design and implement advanced machine learning models for engineering and manufacturing applications
- Build AI systems that understand physics principles, design specifications, and real-world constraints
- Develop solutions that process and analyze complex technical data from physical product development
- Collaborate with a team of world-class engineers to push AI capabilities in hard-tech domains
- Iterate rapidly on models and solutions in a fast-paced startup environment
- Work on systems that reason about engineering tradeoffs and performance optimization
What they're looking for
- Expert Python programming
- Deep learning and neural networks
- Natural language processing (NLP)
- Large language models (LLMs)
- Problem-solving and algorithm design
- Competitive programming (bonus)
- Open-source contributions (bonus)
- Physics or engineering domain knowledge (implied)
Benefits
- Visa sponsorship
- Hybrid work (3 days/week in San Francisco office)
- 401(k) plan
- Medical, dental, and vision insurance
- Flexible paid time off
- Office snacks
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Apiphany
Apiphany builds AI-powered manufacturing applications that transform industrial data into real-time insights for automotive, aerospace, and medtech companies. The company is hiring full-stack engineers and machine learning engineers to develop end-to-end solutions that integrate ML models with factory systems, data pipelines, and physics-based reasoning to optimize engineering decisions.
View all jobs at ApiphanyLikely interview questions
- Walk us through a complex deep learning project you've built in Python. How did you approach debugging and optimizing it?
- Describe your experience working with large language models. Have you fine-tuned or adapted an LLM for a specific domain or task?