Manifold Bio

Manifold Bio

AI/ML Scientist, Protein Foundation Models

Boston, MA or San Francisco, CA

Sponsorship not specified$140k-$225kDetected 78 days ago
PythonNode.jsFull-Stack DevelopmentAWSKubernetesMachine LearningDeep LearningPyTorchscikit-learnPandasNumPyNLPAgentic AIStatisticsClinical Laboratory TechniquesBioinformaticsResearchLab ResearchExperimental DesignAnimal StudiesCommunicationCollaboration

About the role

  • Our team of protein engineers, biologists, and computational scientists works across this full stack to pursue programs both internally and with leading pharma companies.
  • Position Manifold's AI team is actively training protein foundation models on our proprietary experimental datasets.
  • This is an on-site role and can be based in either Boston, Massachusetts or San Francisco, California.

Responsibilities

  • Advance the team's ongoing foundation model training efforts-pretraining, fine-tuning, and evaluating folding, docking, language, and generative design models on Manifold's proprietary experimental data
  • Develop and scale training pipelines for distributed, multi-GPU and multi-node training runs
  • Integrate foundation model outputs into mBER to improve binder design success rates and enable new design capabilities
  • Design and execute ML experiments with clear hypotheses, rigorous evaluation frameworks, and systematic analysis
  • Demonstrated experience pretraining and/or fine-tuning protein foundation models (folding, docking, language models, or generative design) with published or otherwise demonstrable results
  • 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 generative antibody design model, mBER, has already demonstrated controllable de novo binder design across multiple million-scale screening campaigns, and the team is now scaling foundation model capabilities to push well beyond current performance.
  • Your work will directly improve mBER's design capabilities and unlock new modeling paradigms for the broader team.
  • You'll own foundation model projects end-to-end, from architecture selection and training infrastructure to evaluation against real experimental outcomes, while contributing to the team's shared research agenda.

Requirements

  • Experience with large-scale model training: distributed training, multi-GPU/multi-node setups, mixed precision, gradient checkpointing
  • Experience working with protein structure data (PDB, mmCIF) and/or protein sequence datasets
  • Proficiency in Python scientific computing stack (NumPy, Pandas, scikit-learn)

Nice to have

  • Experience with protein generative design methods (e.g., RFdiffusion, ProteinMPNN, flow matching approaches)
  • Experience with protein language models (e.g., ESM family)
  • Experience training on proprietary or domain-specific biological datasets
  • Familiarity with Ray for distributed computing
  • Experience with Kubernetes (EKS) and cloud computing platforms (AWS)
  • Knowledge of protein engineering, directed evolution, or structural biology wet lab techniques
  • Experience working with agentic AI coding tools for fast, parallelized execution of modeling experiments
  • Previous biotech/pharma industry experience

Compensation

  • Final compensation decisions are made using a consistent leveling framework and consider the candidate's experience, interview performance, and expected impact.
  • $140,000-225,000
  • This role is eligible for:
  • Annual performance-based target bonus
  • Our compensation ranges are reviewed annually to ensure alignment with market trends and internal equity.
  • 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.

Benefits

  • Annual performance-based target bonus
  • Comprehensive medical, dental, and vision coverage
  • Flexible paid time off and holidays
  • 2+ years of hands-on experience with PyTorch and/or JAX for deep learning
  • Solid understanding of deep learning architectures (transformers, attention mechanisms, diffusion/flow matching) and optimization techniques
  • This reflects the typical offer range for this role, based on experience, role scope, and internal equity.
  • Perks including on-site gym, onsite lunch, and commuter support

Company info

  • 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.
  • We are looking for an AI/ML Scientist to join this effort.
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