Senior/ Staff AI Research Engineer

About the role

We are seeking an experienced research engineer to join our team. As a critical member of our interdisciplinary group, you will collaborate closely with AI scientists, engineers, and drug discovery experts to develop and deploy AI models that accelerate the identification and development of novel therapeutics. This role offers the opportunity to apply cutting-edge AI techniques in production settings to address pressing challenges in healthcare and contribute to improving human health globally. We are particularly interested in candidates who have demonstrated experience building and delivering machine learning systems in industry environments, where rigor, scalability, and cross-functional collaboration are essential. The ideal candidate has experience translating research ideas into robust, production-ready solutions that drive measurable impact. The role is open to the following locations: San Francisco Bay Area, Seattle.

Responsibilities

• GPUs go brrr. Identify bottlenecks, streamline network chatter, write kernels, and make the hardware fly.
• Experiment and Scale. Help unlock bigger models, longer training horizons, larger datasets, and weirder training dynamics.
• Implement the architecture. New architecture elements mix biological intuition with thinking in tensors. Translate molecules to matrices, and do it in a way that’s maintainable and clean to read.
• Make some drugs. Drug design campaigns involve heavy inference workloads and the inference harness is where molecule designer needs meet ML implementation skill. Build the tools that we use to make drugs.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related fields; advanced degree preferred
• Minimum of five years of industry experience in AI, machine learning, or related roles, with a track record of implementing, scaling, and deploying AI models in production environments.
• Expertise with modern deep learning toolchains and experience with large scale model training.
• Strong understanding of deep learning fundamentals.
• Excellent problem-solving skills, with a keen eye for detail and a passion for tackling complex challenges in the life sciences.
• Effective communication and collaboration abilities, with the capacity to work effectively in a fast-paced, multidisciplinary team environment.
• Publication track record at conferences such as NeurIPS, ICML, or ICLR is a plus, though demonstrated industry impact is prioritized