Manifold Bio is a platform therapeutics company that uses an AI-guided, direct-to-vivo discovery engine to design tissue-targeted biologics that can cross the blood–brain barrier and other physiological barriers.
<div><strong>Manifold Bio is a platform biotechnology company pioneering AI-guided protein design and massively multiplexed in vivo screening to unlock tissue-targeted medicines and organism-scale models of living systems.</strong><span style="color: #262626;"> Using proprietary molecular barcoding technology, we screen hundreds of thousands of protein designs simultaneously in living systems, producing in vivo-validated datasets at a scale no one else can match. The datasets power our computational models, which leads to better drug designs, creating a flywheel that gets stronger with every campaign. Our team of protein engineers, biologists, and computational scientists works</span><span style="color: #262626;"> across this full stack to pursue programs both internally and with leading pharma companies.</span></div> <div> </div> <p><strong>Position</strong></p> <p>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 <em>de novo</em> 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.</p> <p><span style="text-decoration: underline;"><strong>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. </strong></span></p> <p><strong>Responsibilities</strong></p> <ul> <li>Implement and optimize machine learning models for protein design</li> <li>Build and maintain scalable data processing pipelines for large-scale protein and molecular datasets</li> <li>Develop and deploy ML infrastructure for distributed training and inference across GPU clusters</li> <li>Collaborate with research scientists to translate experimental ML approaches into production-ready code</li> <li>Design and execute ML experiments with clear hypotheses and rigorous analysis</li> <li>Optimize model performance and computational efficiency for large-scale protein design tasks</li> <li>Build tools and utilities to support rapid prototyping and experimentation by the research team</li> </ul> <p><strong>Required Qualifications</strong></p> <ul> <li>Bachelor's or Master's degree in Computer Science, Machine Learning, Computational Biology, or related field</li> <li>2+ years of hands-on experience with PyTorch and/or JAX for deep learning applications</li> <li>Strong proficiency in Python scientific computing stack (NumPy, Pandas, scikit-learn)</li> <li>Experience with distributed computing and GPU optimization techniques</li> <li>Familiarity with protein structure analysis, computational biology, or analogous problems in natural sciences</li> <li>Understanding of modern deep learning architectures and optimization techniques</li> <li>Experience implementing research papers or translating ML approaches to production systems</li> <li>Proficiency with version control (Git), testing frameworks, and software engineering best practices</li> <li>Strong problem-solving skills and ability to work independently on technical challenges</li> <li>Excellent written and verbal communication skills for cross-functional collaboration</li> </ul> <p><strong>Preferred Qualifications</strong></p> <ul> <li>Experience training LLMs or diffusion generative models</li> <li>Knowledge of cloud computing platforms (AWS, GCP) and containerization (Docker, Kubernetes)</li> <li>Background in computational biology, bioinformatics, or structural biology</li> <li>Experience with large-scale data engineering and ETL pipelines</li> <li>Familiarity with MLOps practices and model deployment frameworks</li> </ul> <p><strong>This Role Might Be Perfect For You If</strong></p> <p><strong>You are passionate about leveraging state of the art machine learning approaches to solve challenging disease areas</strong></p> <ul> <li>You enjoy translating research ideas into high impact, productionized, scalable code</li> <li>You have rich AI/ML experience and are looking to pivot into biotech</li> </ul> <p><strong>If you're excited to build scalable ML systems that revolutionize protein therapeutic discovery, please reach out to <a href="mailto:careers@manifold.bio">careers@manifold.bio</a>.</strong></p> <p> </p> <p><strong>Base Salary Range: </strong>$140,000-225,000</p> <p>This reflects the typical offer range for this role, based on experience, role scope, and internal equity. Final compensation decisions are made using a consistent leveling framework and consider the candidate’s experience, interview performance, and expected impact.</p> <p>This role is eligible for:</p> <ul> <li>Annual performance-based target bonus</li> <li>Stock options</li> <li>Comprehensive medical, dental, and vision coverage</li> <li>401(k) plan</li> <li>Flexible paid time off and holidays</li> <li>Perks including on-site gym, onsite lunch, and commuter support</li> </ul> <p>Our compensation ranges are reviewed annually to ensure alignment with market trends and internal equity.</p><div class="content-conclusion"><p><em style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-weight: 400; letter-spacing: normal; text-align: start; white-space: normal; word-spacing: 0px; text-decoration: none;"><em><span style="font-weight: 400;">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.</span></em></em></p></div>