Senior / Staff Machine Learning Research Engineer

<h3><strong>Who We Are:</strong></h3> <p>Calico (Calico Life Sciences LLC) is an Alphabet-founded research and development company whose mission is to harness advanced technologies and model systems to increase our understanding of the biology that controls human aging. Calico will use that knowledge to devise interventions that enable people to lead longer and healthier lives. Calico's highly innovative technology labs, its commitment to curiosity-driven discovery science and, with academic and industry partners, its vibrant drug-development pipeline, together create an inspiring and exciting place to catalyze and enable medical breakthroughs.</p> <h3><strong>Position Description:</strong></h3> <p>Calico seeks Machine Learning Research Engineers to join our rapidly growing ML team. You will play a critical role establishing the engineering culture for frontier ML research in drug discovery. You will bring a high level of engineering rigor to our machine learning efforts, and accelerate our research maturing into tangible clinical and product impact.</p> <p>This will be a high agency role designed for a builder who wants to operate as a founding member of a new functional group.&nbsp;</p> <p>Please note: No biology or life sciences background is required for this role.</p> <p><strong>Position Responsibilities:</strong></p> <p>You will drive the engineering vision behind our machine learning models, from identifying high-impact research engineering opportunities to delivering production-grade systems. Partnering closely with&nbsp; biology-fluent ML researchers and Data Engineers, your responsibilities will span the following areas:</p> <ul> <li><strong>Research Engineering:</strong>&nbsp;</li> <ul> <li>Proactively identify emerging engineering gaps required for expanding our research capabilities</li> <li>Architect solutions for complex systems challenges, such as asynchronous execution, hardware orchestration, and high-throughput data pipelines</li> <li>Build foundational libraries that enforce software engineering rigor</li> </ul> <li><strong>Model Implementation and Optimization:</strong>&nbsp;</li> <ul> <li>Translate prototype models (e.g., diffusion networks, vision transformers, and DNA sequence models) into highly-optimized JAX or PyTorch code on accelerators</li> <li>Dive deep into model architectures to optimize training and inference performance, implementing advanced strategies such as model parallelism</li> </ul> <li><strong>Force Multiplier:</strong>&nbsp;</li> <ul> <li>Design and implement evaluation frameworks, benchmarks, and scientific workflow tools that accelerate the research lifecycle and allow the team