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Machine Learning Research Engineer / Scientist

Seattle, Washington, United StatesOnsiteFull Time$120,000–$250,000 /yrPosted 2 months agoHidden Gem · YC Startup

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Role summary

Seeking Machine Learning Research Engineers/Scientists to develop, train, and deploy large-scale AI foundation models for physics-based applications like weather and energy. Responsibilities include architecting ML models for spatiotemporal data, leading end-to-end system development, optimizing training/inference, conducting experiments, and driving research initiatives. Requires strong Python, deep learning framework (PyTorch, Jax), distributed training, large-scale data pipeline, and software engineering skills.

## Role Overview

We are seeking Machine Learning Research Engineers / Scientists to join our team working on groundbreaking physics foundation models. The successful candidate will develop, train and deploy to production large-scale AI foundation models for weather, energy, and beyond. 

## What You'll Do

* Architect and implement innovative ML models for complex spatiotemporal data analysis.
* Lead end-to-end development of large-scale AI systems, from research to production.
* Drive the optimization of training and inference pipelines for maximum performance.
* Conduct validation experiments and performance analysis.
* Spearhead long-term research initiatives with significant real-world impact.
* Collaborate with world-class researchers and engineers.

## We expect you to have

* Proven track record in developing and deploying deep learning models.
* Advanced proficiency in Python and modern ML frameworks (PyTorch, Jax or similar).
* Demonstrated experience with distributed training systems and large-scale data pipelines.
* Strong software engineering practices and system design principles.
* Excellent problem-solving and analytical skills.
* Outstanding communication and collaboration abilities.

## Nice to have

* MSc or PhD in Artificial Intelligence, Computer Science, or related technical field.
* Published research in prestigious AI conferences/journals (NeurIPS, ICML, etc.).
* Hands-on experience with one or several of the following: transformers, diffusion models, self-supervised learning, foundation model training/fine-tuning.

Join us in pushing the boundaries of foundation models for the physical world!
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