---
title: 'Senior Machine Learning Scientist I/II, Model-Driven Optimization at Generate Biomedicines'
canonical: 'https://feeny.ai/job/senior-machine-learning-scientist-i-ii-model-driven-optimization-generate-rtdppx90ewc5'
type: 'job'
last_seen: '2026-10-16'
---

# Senior Machine Learning Scientist I/II, Model-Driven Optimization at Generate Biomedicines

- **Company:** Generate Biomedicines
- **Location:** Somerville, MA
- **Compensation:** $192k–$265k
- **Posted:** 2026-07-14
- **Last confirmed live:** 2026-10-16
- **Apply:** https://generatebiomedicines.com/open-positions?gh_jid=4696856006

## Job description

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 proteins. In 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.

Here's 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 creation—one 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 Company’s 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.

## #LI-HM1

Compensation: The base salary range provided reflects our current estimate of what we anticipate paying for this position. Your actual base salary will be based on several factors, including job-related skills, experience, internal equity, relevant education or training, and market dynamics. In addition, you will be eligible for an annual bonus, equity compensation, and a competitive benefits package.

Per Year Salary Range $192,000—$265,000 USD

## About Generate Biomedicines

## Company Overview
- **One-liner**: Generate Biomedicines is a clinical-stage generative biology company pioneering the use of machine learning to design and develop protein-based medicines on demand.
- **Entity Type**: Public (ticker: GBIO)
- **Headquarters**: Somerville, Massachusetts, USA (Boynton Yards and Andover locations)
- **Founded**: 2018
- **Founders**: Gevorg Grigoryan, Daria Hazuda, and others

## Core Business
- **Primary industry**: Biotechnology / Drug Discovery & Development
- **Target customers**: B2B (pharmaceutical partners) and patients (via clinical programs)
- **Mission statement**: "We don’t discover medicines—we design them," with a belief that speed is a moral obligation to patients.

## Products & Services
- **[Generate Platform™]**: An integrated computational and biohardware innovation stack that uses generative biology to design proteins with defined biological intent. It can generate up to a billion designed variants per cycle with cycle times as fast as ~8 days.
- **[GB-0895 (Anti-TSLP Antibody)]**: A next-generation anti-TSLP monoclonal antibody for severe asthma and COPD, currently in global Phase 3 trials. Designed for ultra-high affinity (106 fM binding, 20x improvement over tezepelumab) and an extended half-life (98 days, longest known among TSLP-targeting therapies), enabling twice-yearly subcutaneous dosing.
- **[GB-5267 (MUC-16 Armored CAR-T)]**: A cell therapy candidate for metastatic ovarian cancer, expected to enter Phase 1 in 1H26.
- **[GB-4362 (Free MMAE Toxin Neutralizer)]**: A first-in-class neutralizer designed for combination with MMAE-based antibody-drug conjugates (ADCs), with Phase 1 initiation in bladder cancer expected 1H26.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed; IPO in February 2026 raised net proceeds of approximately $370 million.
- **Key Metric**: $221.5 million cash balance at YE 2025, plus ~$370 million from IPO (early 2026).
- **Notable Investors/Partners**: Strategic collaborations with Amgen, Novartis, MD Anderson Cancer Center, and Roswell Park Comprehensive Cancer Center.
- **Growth Signals**: 5 clinical-stage programs enabled by the platform; initiated global Phase 3 studies for GB-0895; operating out of 140k+ square feet of lab and office space; has studied millions of proteins and generated 42,000 proteins built and tested.

## Competitive Advantages
- **Fully integrated computational-biohardware loop**: Machine learning drives generative design, which feeds directly into high-throughput experimentation (including proprietary CryoEM with four of the best microscopes in the world), and results continuously train the AI system — a "living feedback loop" that compounds over time.
- **Multimodal platform**: Can generate candidates across antibodies, CAR-T, and other protein modalities from a single core technology, enabling a pipeline that covers both immunology and oncology.
- **Record speed and scale**: Capable of generating up to a billion designed variants in ~8-day cycles, dramatically reducing the time and cost traditionally associated with drug discovery.
- **First-mover in next-gen anti-TSLP**: First known next-generation anti-TSLP antibody to enter the clinic (GB-0895) with a differentiated half-life profile that could enable twice-yearly dosing, a significant competitive edge in a large market.

## Strategic Focus
- **Clinical execution**: Advancing GB-0895 through global Phase 3 trials in severe asthma (with data in 2H27/1H28) and initiating late-stage development in COPD.
- **Pipeline expansion**: Moving GB-5267 (CAR-T) and GB-4362 (ADC neutralizer) into Phase 1 in 2026, expanding the platform's therapeutic scope.
- **Platform scaling**: Continuing to refine the generative biology engine to unlock new modalities and therapeutic areas, while deepening existing strategic collaborations with major pharma partners.
- **Organizational growth**: Building out the team across computational, wet-lab, clinical, and operational functions to support the growing pipeline.

## Why Work Here
- **Mission-driven**: The company is explicit that "speed is not just a competitive advantage—it’s a moral obligation." Employees are directly involved in designing drugs rather than discovering them, with a focus on getting treatments to patients faster.
- **Interdisciplinary culture**: The company emphasizes true integration across machine learning, biological engineering, and medicine. As stated on their site, "We are a close-knit group of cross-disciplinary experts... each conversant in one another’s fields, working together to create a new one."
- **Cutting-edge technology**: Access to unique computational and biohardware infrastructure, including "the four best CryoEM microscopes in the world" and a high-throughput experimental loop that powers AI training in real-time.
- **Values-driven leadership**: Explicit leadership behaviors include "value the knowledge we gain more than the knowledge we have," "bring creativity, tenacity, and ingenuity to every challenge," and "hold oneself and others to an obligation to dissent."
- **Hybrid/office policy**: Not explicitly stated, but the company operates large physical facilities in Somerville and Andover, MA, and offers free parking, monthly subway passes, or subsidized commuter rail passes, suggesting a significant on-site expectation.
- **Comprehensive benefits**: Competitive pay, employee stock option plan, health/vision/dental insurance, long- and short-term disability, life insurance, EAP, retirement savings, paid parental/adoption/caregiver leave, free weekly lunch, and stocked kitchen.
- **Growth trajectory**: As a newly public company with a cash-rich balance sheet and multiple catalysts over the next 12–24 months, the company offers significant career growth potential for those who want to be part of building a "transcendent company" that redefines an industry.

## Sources
1. [generatebiomedicines.com](https://generatebiomedicines.com/)
2. [generatebiomedicines.com/careers](https://generatebiomedicines.com/careers)
3. [generatebiomedicines.com/about-us](https://generatebiomedicines.com/about-us)
4. [investors.generatebiomedicines.com](https://investors.generatebiomedicines.com/)
5. [Non-Confidential Overview (April 2026)](https://investors.generatebiomedicines.com/wp-content/uploads/2026/04/Generate-Biomedicines-Non-Confidential-Overview_April-2026-1.pdf)

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