Manifold Bio is a dynamic biotech company building a pipeline of targeted biologics using a novel in vivo-centric discovery approach. Our drug discovery engine is differentiated by massively parallel screening in vivo from the beginning of our discovery process. This unique platform is powered by a proprietary protein barcoding technology that allows multiplexed protein quantitation at unprecedented scale and sensitivity. We combine this and other high-throughput protein engineering approaches 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.

Please read the information in this job post thoroughly to understand exactly what is expected of potential candidates. Manifold 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. Responsibilities Implement and optimize machine learning models for protein design. Build and maintain scalable data processing pipelines for large-scale protein and molecular datasets. Develop and deploy ML infrastructure for distributed training and inference across GPU clusters. Collaborate with research scientists to translate experimental ML approaches into production-ready code. Design and execute ML experiments with clear hypotheses and rigorous analysis. Optimize model performance and computational efficiency for large-scale protein design tasks. Build tools and utilities to support rapid prototyping and experimentation by the research team. Required Qualifications Bachelor's or Master's degree in Computer Science, Machine Learning, Computational Biology, or related field 2+ years of hands-on experience with PyTorch and/or JAX for deep learning applications Strong proficiency in Python scientific computing stack (NumPy, Pandas, scikit-learn) Experience with distributed computing and GPU optimization techniques Familiarity with protein structure analysis, computational biology, or analogous problems in natural sciences Understanding of modern deep learning architectures and optimization techniques Experience implementing research papers or translating ML approaches to production systems Proficiency with version control (Git), testing frameworks, and software engineering best practices Strong problem-solving skills and ability to work independently on technical challenges Excellent written and verbal communication skills for cross-functional collaboration Preferred Qualifications Experience with transformer architectures or graph neural networks for molecular data Knowledge of cloud computing platforms (AWS, GCP) and containerization (Docker, Kubernetes) Background in computational biology, bioinformatics, or structural biology Experience with large-scale data engineering and ETL pipelines Familiarity with MLOps practices and model deployment frameworks 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. Apply for this job * indicates a required field First Name * Last Name * Preferred First Name Email * Phone Resume/CV Enter manually Accepted file types: pdf, doc, docx, txt, rtf Enter manually Accepted file types: pdf, doc, docx, txt, rtf

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