---
title: 'AI/ML Research Engineer at Manifold Bio'
canonical: 'https://feeny.ai/job/ai-ml-research-engineer-manifold-bio-boston-ma-or-san-francisco-0qe65tmrvvjb'
type: 'job'
last_seen: '2026-09-13'
---

# AI/ML Research Engineer at Manifold Bio

- **Company:** Manifold Bio
- **Location:** Boston MA OR San Francisco, CA
- **Posted:** 2026-04-13
- **Last confirmed live:** 2026-09-13
- **Apply:** https://job-boards.greenhouse.io/manifoldbio/jobs/5106191007

## Job description

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 works across this full stack to pursue programs both internally and with leading pharma companies.

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.

Base Salary Range: $140,000-225,000

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.

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.

## About Manifold Bio

## Company Overview
- **One-liner**: Manifold Bio is a platform therapeutics company building the first AI-guided direct-to-vivo discovery platform to design tissue-targeted biologics.
- **Entity Type**: Private (Series A)
- **Headquarters**: Boston, Massachusetts, United States
- **Founded**: 2019
- **Founders**: Gleb Kuznetsov (CEO), Pierce Ogden (CTO), Shane Lofgren (Head of Business Development)

## Core Business
- **Primary industries**: Biotechnology, Drug Discovery, Artificial Intelligence for Protein Design
- **Target customers**: B2B – large pharmaceutical companies (via partnerships and collaborations) and internal pipeline for novel therapeutics
- **Mission or purpose statement**: “Tissue-targeted medicines, designed in vivo” – building a predictive model of living systems to enable rational design of biologics that precisely reach intended tissues.

## Products & Services
- **mDesign Engine**: Integrated platform combining AI-guided protein design with massively multiplexed in vivo screening to measure pharmacokinetics, biodistribution, and target engagement directly in living systems.
- **mCodes**: Multiplexed protein barcodes decoded via NGS, enabling high-throughput tracking of millions of protein variants in a single experiment.
- **mShuttle Portfolio**: Modular library of brain shuttles engineered and optimized in vivo to deliver payloads across the blood-brain barrier with high specificity and exposure.
- **mBER**: Open-sourced AI model for designing epitope-specific antibodies, validated in million-scale experiments. (SaaS/model, not a product per se)
- **Internal Pipeline**: Tissue-targeted biologic candidates for Alzheimer’s disease, Parkinson’s disease, rare CNS diseases, and cardiometabolic disease.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Total Funding**: $78.18M (CBInsights) / $61.4M (Apollo.io) – conflicting reports; latest available data shows multiple rounds including a $15M Series A in April 2024.
- **Notable Investors/Partners**: Playground Global, Fifty Years, Amazon Web Services, and a strategic collaboration with Roche announced in November 2025.
- **Growth Signals**: Headcount grew 58.1% YoY to 54 employees; published a joint study with NVIDIA validating million-scale AI-driven protein binder design; active job postings up 100% monthly.

## Competitive Advantages
- **Direct-to-vivo platform**: Unlike most AI-driven drug discovery companies that rely on in vitro data, Manifold tests protein designs directly in living systems, capturing physiologically relevant properties like PK, biodistribution, and toxicity.
- **Massively multiplexed experimentation**: Proprietary molecular tracking (mCodes) enables simultaneous testing of millions of protein variants against hundreds of targets, closing the loop between generative AI design and experimental validation at scale.
- **High-profile collaborations**: The Roche partnership and NVIDIA validation provide external credibility and access to industry-leading resources.

## Strategic Focus
- **Expanding tissue-specific delivery**: Starting with brain (CNS) through mShuttle, then moving to peripheral tissues (adipose, cardiometabolic).
- **Building the “virtual organism”**: Integrating high-throughput in vivo data to train a predictive model of how biomolecules behave in the body, enabling fully rational design.
- **Deepening pharma partnerships**: Leveraging platform capabilities to co-develop next-generation biologics with partners like Roche.
- **Open-sourcing AI models**: Releasing mBER and other tools to attract talent, drive adoption, and set industry standards.

## Why Work Here
- **Culture**: Flat, science-driven organization where ML engineers, computational biologists, and wet-lab scientists collaborate closely. Emphasis on “hybrid computational/wet lab science” and library-guided design.
- **Growth trajectory**: 58% employee growth YoY, active hiring across AI/ML, protein engineering, biology, and operations – signals rapid scaling.
- **Work environment**: Boston-based (Seaport area), likely office-first with some flexibility; job postings don’t mention remote but roles are Boston-listed.
- **Notable perks**: Opportunity to work at the frontier of AI + biology, access to cutting-edge wet lab infrastructure, and direct impact on pipeline decisions from early discovery through IND-enabling studies.

## Sources
1. [manifold.bio](https://www.manifold.bio/) (Company website)
2. [linkedin.com](https://www.linkedin.com/company/manifold-bio) (LinkedIn company page)
3. [cbinsights.com](https://www.cbinsights.com/company/manifold-bio) (CBInsights profile)
4. [boards.greenhouse.io](http://job-boards.greenhouse.io/manifoldbio) (Careers page)
5. [manifold.bio/news](https://www.manifold.bio/news) (News – Roche collaboration, NVIDIA study)
6. [apollo.io](https://www.apollo.io/companies/Manifold-Bio) (Apollo.io revenue/funding data)

## Other roles at Manifold Bio

- [Senior Lab Operations Associate](https://feeny.ai/job/senior-lab-operations-associate-manifold-bio-boston-9hzaxy9hgc0z) — Boston, MA
- [Scientist II/Senior Scientist, Protein Sciences – Cell Binding Assays](https://feeny.ai/job/scientist-ii-senior-scientist-protein-sciences-cell-binding-assays-manifold-bio-he5xybzbd1mk) — Boston, MA
- [Scientist, Bioconjugation](https://feeny.ai/job/scientist-bioconjugation-manifold-bio-boston-v1hw9b634cb4) — Boston, MA
- [Research Associate / Senior Research Associate, In Vitro Pharmacology](https://feeny.ai/job/research-associate-senior-research-associate-in-vitro-pharmacology-manifold-bio-a5vxzqjbgfkm) — Boston, MA
- [Associate Scientist/Senior Associate Scientist, In Vivo Pharmacology – Study Coordinator](https://feeny.ai/job/associate-scientist-senior-associate-scientist-in-vivo-pharmacology-study-vs7tg31p2che) — Boston, MA
- [Computational Scientist, Assay Development](https://feeny.ai/job/computational-scientist-assay-development-manifold-bio-boston-4qvj0atdygfs) — Boston, MA
- [Product Manager, AI Platform & Partnerships](https://feeny.ai/job/product-manager-ai-platform-partnerships-manifold-bio-boston-pghg83twvq9t) — Boston, MA / San Francisco, CA
- [Sr. Director/VP, Biology](https://feeny.ai/job/sr-director-vp-biology-manifold-bio-boston-f5c0ac39wzn7) — Boston, MA
- [AI/ML Scientist, Protein Foundation Models](https://feeny.ai/job/ai-ml-scientist-protein-foundation-models-manifold-bio-boston-ma-or-san-ndnwdy540hyp) — Boston MA OR San Francisco, CA
- [Senior Director, Biotherapeutics](https://feeny.ai/job/senior-director-biotherapeutics-manifold-bio-boston-s6aww0xdkqqd) — Boston, MA