Principal Machine Learning Engineer

About

Edison Scientific builds and commercializes AI agents for science. Scientific discovery moves too slowly, and autonomous AI agents are how we intend to fix that. We're assembling a team of top researchers and engineers across AI and biology to build an AI scientist.

Role

As a Principal 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.

This role is on-site at our San Francisco office in the Dogpatch neighborhood. Our office is a converted warehouse with high ceilings, open space, and a team excited about what we’re building.

Responsibilities

• 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.

Qualifications

• 8-10+ 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.

Bonus points for

• PhD in Machine Learning, Computer Science, or other quantitative field
• Familiarity with leveraging and managing distributed computing resources
• Background architecting complex distributed systems