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# Member of Technical Staff, Machine Learning Engineer

- **Company:** EDISON LLC
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $240,000 - $350,000
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Computer Programming, Distributed Systems, Machine Learning, Pytorch, Large Language Models, Deep Learning, Generative AI, Agentic-AI, Information Technology, Machine Learning Operations, Data Pipelines
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25304_5e38a2c8252f60326b7dd0be963e8cb8

## About the Role

* 5-10+ years of strong track record of work in applied ML research and application of ML methods to solving real-world problems
 * Experience working across the ML lifecycle: data pipelines and provenance, model training, model deployment, and validation in production systems.
 * Fluency in PyTorch, Jax or equivalent framework.
 * Demonstrated experience with experimentation in academic or industry settings.
 * Strong programming expertise with the capability to adapt to various technical challenges in the data, ML, and LLM software stack., * PhD in Machine Learning, Computer Science, or other quantitative field
 * Familiarity with leveraging and managing distributed computing resources
 * Background architecting complex distributed systems

## Description

As a Member of Technical Staff, Machine Learning Engineer at Edison Scientific, you play a central role in building the models and agents that accelerate scientific discovery. You will work on both cutting edge research and practical engineering, bridging advanced machine learning concepts with robust, reliable software that real scientists depend on., * Interpret qualitative challenges in building AI agents for science as well-formulated optimizable problems

 * Build appropriate environments in which to train and deploy AI agents that solve scientific tasks
 * Work with scientists to formulate training data pipelines, and scale them, ensuring observability and reproducibility
 * Lead training of large-scale LLM-based systems, including building internal infrastructure to improve the efficiency of experimentation and production training runs
 * Build efficient and flexible inference infrastructure, supporting complex sampling algorithms and custom architectures
 * Develop and extend our experimentation platform for internal tools and projects.
 * Collaborate closely with a multidisciplinary team of AI researchers, chemists, biologists, fostering an environment of innovation and discovery.