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. 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 worksacross this full stack to pursue programs both internally and with leading pharma companies.
Position
Manifolds AI team is actively training protein foundation models on our proprietary experimental datasets. 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. We are looking for an AI/ML Scientist to join this effort. You will work alongside our existing model training team to accelerate the development of foundation models fine-tuned on Manifolds data bringing additional depth in pre-training methodology architecture development and large-scale training. Your work will directly improve mBERs design capabilities and unlock new modeling paradigms for the broader team. Youll own foundation model projects end-to-end from architecture selection and training infrastructure to evaluation against real experimental outcomes while contributing to the teams shared research agenda.
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
Advance the teams ongoing foundation model training effortspretraining fine-tuning and evaluating folding docking language and generative design models on Manifolds proprietary experimental data
Bring depth in training methodology architecture selection and optimization to complement the existing teams expertise
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
Establish best practices for mixed-precision training gradient checkpointing and computational efficiency at scale
Produce clear documentation and analysis supporting architecture and training decisions
Required Qualifications
Demonstrated experience pretraining and/or fine-tuning protein foundation models (folding docking language models or generative design) with published or otherwise demonstrable results
Strong familiarity with AlphaFold architecture and training methodology
2 years of hands-on experience with PyTorch and/or JAX for deep learning
Experience with large-scale model training: distributed training multi-GPU/multi-node setups mixed precision gradient checkpointing
Solid understanding of deep learning architectures (transformers attention mechanisms diffusion/flow matching) and optimization techniques
Experience working with protein structure data (PDB mmCIF) and/or protein sequence datasets
Strong statistical analysis and experimental design skills
Proficiency in Python scientific computing stack (NumPy Pandas scikit-learn)
Self-directed researcher who can balance guidance with independence
Excellent written and verbal communication skills for cross-functional collaboration
Preferred Qualifications
Experience with protein generative design methods (e.g. RFdiffusion ProteinMPNN flow matching approaches)
Experience with protein language models (e.g. ESM family)
Published research in computational biology protein design or structural biology
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
This Role Might Be Perfect For You If:
You have deep experience training protein foundation models and want to apply that expertise to some of the richest proprietary experimental datasets in the field
Youre excited about pushing beyond public model performance by leveraging unique large-scale in vivo screening data
You thrive in high-ownership roles where you can drive research direction while collaborating with a tight-knit world-class team
You want your models to directly impact real drug discovery programs
If youre excited to train the next generation of protein foundation models on uniquely powerful experimental data please reach out to .
Base Salary Range: $00
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 candidates experience interview performance and expected impact.
This role is eligible for:
Annual performance-based target bonus
Stock options
Comprehensive medical dental and vision coverage
401(k) plan
Flexible paid time off and holidays
Perks including on-site gym onsite lunch and commuter support
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.
Required Experience:
IC
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. Using proprietary molecular barcoding technology we screen hundreds of thousands of protei...
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. 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 worksacross this full stack to pursue programs both internally and with leading pharma companies.
Position
Manifolds AI team is actively training protein foundation models on our proprietary experimental datasets. 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. We are looking for an AI/ML Scientist to join this effort. You will work alongside our existing model training team to accelerate the development of foundation models fine-tuned on Manifolds data bringing additional depth in pre-training methodology architecture development and large-scale training. Your work will directly improve mBERs design capabilities and unlock new modeling paradigms for the broader team. Youll own foundation model projects end-to-end from architecture selection and training infrastructure to evaluation against real experimental outcomes while contributing to the teams shared research agenda.
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
Advance the teams ongoing foundation model training effortspretraining fine-tuning and evaluating folding docking language and generative design models on Manifolds proprietary experimental data
Bring depth in training methodology architecture selection and optimization to complement the existing teams expertise
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
Establish best practices for mixed-precision training gradient checkpointing and computational efficiency at scale
Produce clear documentation and analysis supporting architecture and training decisions
Required Qualifications
Demonstrated experience pretraining and/or fine-tuning protein foundation models (folding docking language models or generative design) with published or otherwise demonstrable results
Strong familiarity with AlphaFold architecture and training methodology
2 years of hands-on experience with PyTorch and/or JAX for deep learning
Experience with large-scale model training: distributed training multi-GPU/multi-node setups mixed precision gradient checkpointing
Solid understanding of deep learning architectures (transformers attention mechanisms diffusion/flow matching) and optimization techniques
Experience working with protein structure data (PDB mmCIF) and/or protein sequence datasets
Strong statistical analysis and experimental design skills
Proficiency in Python scientific computing stack (NumPy Pandas scikit-learn)
Self-directed researcher who can balance guidance with independence
Excellent written and verbal communication skills for cross-functional collaboration
Preferred Qualifications
Experience with protein generative design methods (e.g. RFdiffusion ProteinMPNN flow matching approaches)
Experience with protein language models (e.g. ESM family)
Published research in computational biology protein design or structural biology
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
This Role Might Be Perfect For You If:
You have deep experience training protein foundation models and want to apply that expertise to some of the richest proprietary experimental datasets in the field
Youre excited about pushing beyond public model performance by leveraging unique large-scale in vivo screening data
You thrive in high-ownership roles where you can drive research direction while collaborating with a tight-knit world-class team
You want your models to directly impact real drug discovery programs
If youre excited to train the next generation of protein foundation models on uniquely powerful experimental data please reach out to .
Base Salary Range: $00
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 candidates experience interview performance and expected impact.
This role is eligible for:
Annual performance-based target bonus
Stock options
Comprehensive medical dental and vision coverage
401(k) plan
Flexible paid time off and holidays
Perks including on-site gym onsite lunch and commuter support
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.
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