Member of Technical Staff, Machine Learning Engineer
AI summary of the role
Edison Scientific is hiring a Member of Technical Staff, Machine Learning Engineer to build AI scientist agents for accelerating drug discovery.
What you’ll do
- Interpret qualitative challenges in building AI agents for science as well-formulated optimizable problems
- Build environments to train and deploy AI agents that solve scientific tasks
- Scale training data pipelines with scientists, ensuring observability and reproducibility
- Lead training of large-scale LLM-based systems and build internal infrastructure for efficient experimentation
What you’ll bring
- 5-10+ years of applied ML research and real-world ML application
- Experience across the ML lifecycle: data pipelines, training, deployment, validation in production
- Fluency in PyTorch, Jax, or equivalent framework
- Demonstrated experimentation experience in academic or industry settings
Technologies
PyTorch · Jax · LLM · distributed computing · data pipelines · inference infrastructure · experimentation platform
Source and classification
Internal deployment & tooling · Evidence for this classification:
About us Edison Scientific builds and deploys AI scientist agents to accelerate science and the development of new medicines. We are an ambitious team run by scientists and engineers from leading institutions across biology, physics, chemistry, and AI. Role 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. 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
More from the job description
About us Edison Scientific builds and deploys AI scientist agents to accelerate science and the development of new medicines. We are an ambitious team run by scientists and engineers from leading institutions across biology, physics, chemistry, and AI. Role 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. 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 sampli [... source excerpt omitted ...] for internal tools and projects. Collaborate closely with a multidisciplinary team of AI researchers, chemists, biologists, fostering an environment of innovation and discovery. Qualifications 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
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