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.
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.
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.
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.
PhD in Machine Learning, Computer Science, or other quantitative field
Familiarity with leveraging and managing distributed computing resources
Background architecting complex distributed systems
Salary
$275,000 - $350,000 • Offers equity
Why join us?
Competitive salary and equity
Full healthcare coverage — we pay 100% of premiums for you and your dependents
Support for growing families, including a yearly new parent stipend and fertility coverage through Carrot
401(k) company matching
$300 health and wellness benefit
Lunch is on us every day you're in the office, and dinner is on us when you're working late
Regular team offsites and company events
A fast-moving, mission-driven culture where smart people do their best work and actually enjoy doing it
Edison%20scientific
https://edison%20scientific.com