AI/ML Infrastructure Engineer

Responsibilities

• Own and develop Manifold's EKS-based compute platform to meet the shifting needs of our computational sub-teams - mBER development and production runs, LLM fine-tuning, novel binder design research, and more
• Monitor AWS compute costs and implement optimizations that reduce spend while supporting continued growth
• Run and optimize production models (mBER, folding models, and other generative models) for fast iteration and a consistent library design cycle
• Improve security, uptime, and cross-region access across the compute stack, hardening infrastructure against external threats
• Establish CI/CD practices (likely GitOps) and clear, comprehensive cost-tracking
• Build and maintain platforms for agentic automation and custom internal agentic workflows
• Help define the data handoff from AI generation to Snowflake + Benchling and connect to experimental readouts
• Required Qualifications
• Strong, ML-specific coding skills in PyTorch and/or JAX, with the ability to quickly prototype, test, and debug
• Strong familiarity with AWS, especially EC2, EKS, networking, and storage solutions
• Experience optimizing GPU-heavy computational workloads
• Strong security practices and experience hardening web applications and infrastructure against external attackers
• Deep integration with agentic AI development tools
• Experience building and working with relational databases
• Ability to move fast - standing up prototypes and iterating in production with a diverse user base
• Strong data science and analysis skills
• Interest in bio-specific ML, with a background in physical or natural sciences

Requirements

• Track record of advanced automation using agentic AI tooling
• Experience with transformer architectures or graph neural networks for molecular data
• Published research in ML, computational biology, or protein design
• Knowledge of protein engineering, directed evolution, or structural biology wet lab techniques
• Previous biotech/pharma industry experience
• This Role Might Be Perfect For You If
• You want to own the compute backbone that powers an entire AI-driven drug discovery platform
• You like working close to the models - not just keeping infrastructure alive, but making it faster and cheaper so scientists can move
• You're energized by agentic AI tooling and want to build the platforms that let a team deploy it at scale
• You have rich ML infrastructure / MLOps experience and are excited to bring it into biotech
• If you're excited to build and scale the infrastructure that powers protein foundation model development, please reach out to careers@manifold.bio.
• 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.

Additional Information

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's AI research runs on a shared, scaled compute platform built on AWS EKS, Ray, and Kubernetes. Today it supports 25+ users across the company with secure, centralized access to data and democratized GPU access - nearly all of our AI research runs here, along with a large share of our bioinformatics work, including hit calling and data ETL pipelines. As we scale mBER and our broader model development toward proteome-scale design, we're looking for an engineer to own and evolve this platform. You'll take full ownership of our scaled computational infrastructure - security, uptime, and cost - while developing a deep enough understanding of the models we deploy to drive runtime optimization and quality-of-life features that make our scientists faster. You'll also build stable infrastructure for deploying custom agentic workflows internally, working hands-on with agentic AI tooling for fast iteration. 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.

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