Senior Machine Learning Scientist I, Model-Driven Optimization

The Role:

Generate:Biomedicines is seeking a creative rigorous and execution-oriented machine learning scientist to join our Model-Driven Design team. This role will focus on building the ML methods data strategies and closed-loop systems that determine what we design build test and learn from next.

The Model-Driven Design team works at the interface of machine learning protein design engineering and experimental science. We develop and apply models and quantitative frameworks that help Generate discover and optimize therapeutic this role you will help advance the technical foundation of our lab-in-the-loop protein optimization platform with a focus on sequential decision-making experimental design property modeling and scalable design systems.

We are looking for someone who can serve as a technical leader and hands-on individual contributor driving complex high-impact work from problem framing through implementation deployment and experimental impact. The ideal candidate combines depth in probabilistic machine learning Bayesian optimization active learning or related approaches with the practical judgment and engineering discipline to turn technical ideas into reliable systems that drive impact. You will partner closely with protein designers wet-lab scientists ML scientists and engineers to build durable capabilities that accelerate therapeutic discovery.

This role is part of a highly collaborative team environment that balances in-person collaboration with hybrid flexibility based out of our Somerville MA office.

Heres how you will contribute:

• Develop new machine learning methods and systems for lab-in-the-loop protein optimization including property models and multi-objective optimization strategies for therapeutic protein design.
• Shape data-generation and data-use strategies that make experimental campaigns maximally informative for model improvement therapeutic optimization and future design cycles.
• Build and apply LLM-enabled and agentic workflows that help scientists explore design hypotheses connect models to data and experiments and accelerate iterative learning.
• Design implement test and maintain production-quality ML models software components and data workflows with attention to reliability reproducibility observability and computational efficiency.
• Partner with ML engineering and software teams to integrate these components into robust scalable platform capabilities with clear ownership across team boundaries.
• Collaborate closely with protein designers and wet-lab scientists to ensure models and optimization systems are grounded in experimental reality and deliver measurable impact.
• Identify important technical gaps develop proposals define milestones align stakeholders and help set technical direction across cross-functional programs.
• Communicate clearly across disciplines and help raise technical standards across ML engineering protein design and experimental teams.

The Ideal Candidate will have:

• PhD in machine learning computational biology computer science applied mathematics engineering or a related quantitative field.
• Strong practical experience with probabilistic machine learning Bayesian optimization active learning experimental design or related approaches for sequential decision-making under uncertainty.
• Experience developing machine learning methods or systems for biological biomedical or experimental scientific data with an ability to reason about noisy assays sparse labels experimental bias and data-generation strategy.
• Demonstrated ability to translate ML ideas into systems tools or workflows that affect real scientific experimental or product decisions.
• Strong Python skills and experience with modern ML frameworks such as PyTorch JAX or similar tools.
• Strong systems thinking and ability to design technical interfaces reason about system tradeoffs and partner with engineering teams to build scalable maintainable ML infrastructure.
• Excellent communication skills and ability to bridge ML engineering protein design and experimental stakeholders.
• Pragmatic collaborative working style with the ability to bring structure to open-ended problems and balance scientific rigor with execution in fast-moving cross-functional environments.

Nice to have

• Experience in protein design protein engineering antibody engineering biologics discovery or drug development.
• Experience partnering with experimental teams on design-build-test-learn cycles high-throughput screening directed evolution pooled libraries or model-guided experimental campaigns.
• Experience with multi-objective optimization uncertainty calibration model-guided library design or experimental campaign planning.
• Experience developing and applying deep learning models including transformer-based architectures
• Experience building or applying LLM agents scientific copilots or agentic systems in technical workflows.
• Experience contributing to shared ML platforms libraries APIs or developer tooling including monitoring debugging performance optimization and long-term maintenance.

Who Will Love This Job:

This is an opportunity to shape how machine learning is used to make better decisions across the full protein design cycle. You will work on problems where models data experiments and engineering systems are tightly connected and where better optimization strategies can directly change what gets built and tested in the lab.

You will join a collaborative ambitious team working to build a platform for therapeutic protein design that learns continuously from experimental data and turns that learning into new and better therapeutics with real impact.

About Generate Biomedicines

We are a clinical-stage generative biology company pioneering the AI revolution in drug design and development. We are advancing a new approach to drug creationone grounded in the ability to design proteins with defined biological intent. By integrating machine learning with large-scale experimentation this approach aims to reduce the uncertainty time and cost associated with developing protein-based medicines.

Founded in 2018 we are advancing a growing pipeline of clinical and preclinical programs across multiple disease areas and protein modalities. By unifying computational design and clinical development within a single operating model we translate this approach into clinical-stage programs and are leading a shift from traditional drug discovery toward systematic drug generation.

At Generate:Biomedicines we collaborate across disciplines in new ways to invent and innovate. We bring diverse perspectives to a shared goal of delivering better medicines to patients in need faster guided by our values and leadership behaviors.

Generate:Biomedicines is committed to equal employment opportunity regardless of race color ancestry religion sex national origin sexual orientation age citizenship marital status disability gender identity or Veteran status.

Recruitment & Staffing Agencies: Generate:Biomedicines does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Generate:Biomedicines or its employees is strictly prohibited unless contacted directly by the Companys internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Generate:Biomedicines and the Company will not owe any referral or other fees with respect thereto.

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