AI/ML Research Engineer

Manifold Bio builds AI models for protein therapeutic design, trained on proprietary experimental data generated at unprecedented scale. Our in vivo-centric discovery platform produces millions of experimentally validated protein designs per campaign, creating the datasets that make our models possible and our approach uniquely powerful. We combine high-throughput protein engineering with computational design to create antibody-like drugs and other biologics. Our world-class team of protein engineers, biologists, and computational scientists are working together to aim the platform at therapeutic opportunities where precise targeting is the key to overcoming clinical challenges.PositionManifold Bio is seeking a talented Machine Learning Research Engineer to join our growing AI team. You will work closely with our research scientists to implement, scale, and optimize machine learning systems that power our de novo antibody design platform and advance our protein design capabilities. Your efforts will contribute to building production-ready ML infrastructure that enables breakthrough discoveries in protein therapeutics. You will be expected to take ownership of engineering challenges in our ML pipeline, from data processing and model training to deployment and monitoring, while collaborating closely with our research team to translate cutting-edge ideas into robust, scalable systems.This is an on-site role and can be based in either Boston, Massachusetts or San Francisco, California. Please only apply if you reside in these cities or are open to relocate. ResponsibilitiesImplement and optimize machine learning models for protein designBuild and maintain scalable data processing pipelines for large-scale protein and molecular datasetsDevelop and deploy ML infrastructure for distributed training and inference across GPU clustersCollaborate with research scientists to translate experimental ML approaches into production-ready codeDesign and execute ML experiments with clear hypotheses and rigorous analysisOptimize model performance and computational efficiency for large-scale protein design tasksBuild tools and utilities to support rapid prototyping and experimentation by the research teamRequired QualificationsBachelor's or Master's degree in Computer Science, Machine Learning, Computational Biology, or related field2+ years of hands-on experience with PyTorch and/or JAX for deep learning applicationsStrong proficiency in Python scientific computing stack (NumPy, Pandas, scikit-learn)Experience with distributed computing and GPU optimization techniquesFamiliarity with protein structure analysis, computational biology, or analogous problems in natural sciencesUnderstanding of modern deep learning architectures and optimization techniquesExperience implementing research papers or translating ML approaches to production systemsProficiency with version control (Git), testing frameworks, and software engineering best practicesStrong problem-solving skills and ability to work independently on technical challengesExcellent written and verbal communication skills for cross-functional collaborationPreferred QualificationsExperience training LLMs or diffusion generative modelsKnowledge of cloud computing platforms (AWS, GCP) and containerization (Docker, Kubernetes)Background in computational biology, bioinformatics, or structural biologyExperience with large-scale data engineering and ETL pipelinesFamiliarity with MLOps practices and model deployment frameworksThis Role Might Be Perfect For You IfYou are passionate about leveraging state of the art machine learning approaches to solve challenging disease areasYou enjoy translating research ideas into high impact, productionized, scalable codeYou have rich AI/ML experience and are looking to pivot into biotechIf you're excited to build scalable ML systems that revolutionize protein therapeutic discovery, please reach out to careers@manifold.bio.We value different experiences and ways of thinking and believe the most talented teams are built by bringing together people of diverse cultures, genders, and backgrounds.