AI/ML Research Engineer at Manifold Bio in Boston, MA

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.

Position

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.

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. 

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 training LLMs or diffusion generative models
• 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

This Role Might Be Perfect For You If

You are passionate about leveraging state of the art machine learning approaches to solve challenging disease areas

• You enjoy translating research ideas into high impact, productionized, scalable code
• You have rich AI/ML experience and are looking to pivot into biotech

If you're excited to build scalable ML systems that revolutionize protein therapeutic discovery, please reach out to careers@manifold.bio.