Applied AI Research Fellow
4/30/2026
The Research Fellow will drive the evolution of Evozyne’s generative AI platform by benchmarking models and exploring advanced optimization methods. They will work on foundational questions in representation learning and generative modeling to inform therapeutic protein design.
Working Hours
40 hours/week
Company Size
51-200 employees
Language
English
Visa Sponsorship
No
Evozyne is one of the few AI-native biotech companies designing de novo therapeutic proteins and advancing them toward the clinic. Our teams apply AI to develop novel therapies within complex biological systems where data is imperfect, and discoveries have meaningful impact on patients’ lives. We are transforming how the industry approaches protein engineering.
Our platform, EvoGen, is both generative and predictive. Rather than focusing on structure alone, we build models that learn how protein sequence drives function, enabling the design of novel proteins optimized across multiple objectives, including potency, stability, specificity, and immunogenicity. Our model development is tightly integrated with proprietary experimental data, enabling rapid learning from biological reality. We combine sequence-based models and variational autoencoders (VAEs) with Bayesian optimization, using experimental data to rapidly design and refine proteins into impactful therapeutics.
The Applied AI Research Fellowship at Evozyne is designed for researchers who want to stress‑test ambitious ideas against one of the most challenging frontiers in applied AI today: generative design under real‑world biological constraints. As a Fellow, you will work on foundational questions in representation learning, generative modeling, and optimization, with the opportunity to see your ideas evaluated against real experimental outcomes and translated into therapeutic programs.
Your work will directly influence how Evozyne evaluates, evolves, and deploys its generative AI models for protein design.
Who You Are
You’re excited by problems where the data is messy, the constraints are real, and the path forward isn’t obvious. You thrive in ambiguity, and you’re motivated by applying your work to real-world scientific challenges to see how your ideas hold up in practice. You are already operating at the leading edge of applied AI and want to push your thinking further by applying it to complex, high-impact challenges in drug discovery.
What You’ll Be Investigating
As an Applied AI Research Fellow, you will help drive the evolution of Evozyne’s generative AI design platform. Example research areas include:
- Benchmarking generative protein models, including Evozyne’s own, on their ability to produce functionally diverse and biologically meaningful designs.
- Evaluating the value of integrating large-scale metagenomic resources (e.g., Global Ocean Gene Catalog) into current internal database.
- Exploring alternatives and extensions to Bayesian Optimization such as Knowledge Gradient, Entropy Search, and related methods for multi-objective optimization problems.
- Developing and applying deep learning approaches for remote homology detection
- Investigating multi-family VAE models to enable protein design when sequence support is limited or when optimizing phenotypes across protein families.
These efforts are intended to surface failure modes, challenge assumptions, and directly inform how Evozyne designs proteins and advances therapies.
Education + Experience
- Late-stage PhD student or postdoc in a quantitative or computational field
- Hands-on experience applying AI/ML to complex, real-world or scientific datasets
- Experience working on problems where data is noisy, incomplete, or difficult to interpret
- Familiarity with modern machine learning approaches (e.g., deep learning, generative models, or related methods)
- Evidence of meaningful contribution to research, open-source work, or applied projects
- Exposure to interdisciplinary work (e.g., biology, chemistry, physics, or other scientific domains) is a plus
Why Evozyne
Few places offer the combination of proprietary experimental data, real therapeutic programs, and the freedom to explore foundational AI questions under real biological constraints. If you want your best ideas tested where they matter most, and the chance to help redefine how AI is applied to protein design, we’d like to connect.
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