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Generative Ai Biology Jobs (NOW HIRING)

Evozyne

Applied AI Research Fellow

Chicago, IL · On-site +1

Applied AI Research Fellowship At Evozyne Evozyne is one of the few AI-native biotech companies ... generative design under real-world biological constraints. As a Fellow, you will work on ...

Evozyne

Applied AI Research Fellow

Chicago, IL · On-site +1

Applied AI Research Fellowship At Evozyne Evozyne is one of the few AI-native biotech companies ... generative design under real-world biological constraints. As a Fellow, you will work on ...

EVOZYNE INC

Applied AI Research Fellow

Chicago, IL · On-site

Our teams apply AI to develop novel therapies within complex biological systems where data is ... generative design under real‑world biological constraints. As a Fellow, you will work on ...

GenBio AI

Deep Learning Engineer (Palo Alto)

Palo Alto, CA · On-site

$170K - $260K/yr

Join us as we embark on this journey to redefine the future of biology and medicine through the transformative power of Generative AI. Key Responsibilities * Design, develop, optimize, and maintain ...

Latent Labs

Computational Protein Designer

San Francisco, CA · On-site

$24.25 - $29.50/hr

At Latent Labs you will be working with some of the brightest minds in generative AI and biology. Our team is committed to interdisciplinary exchange, continuous learning and collaboration. Team ...

Waters Corporation

AI Architect

Milford, MA · On-site

This includes generative AI, machine learning, automation, and future agent-based capabilities ... and biology. We collaborate with customers around the world to advance the release of effective ...

New

Waters Corporation

AI Architect

Milford, MA · On-site

$69 - $91/hr

This includes generative AI, machine learning, automation, and future agent-based capabilities ... and biology. We collaborate with customers around the world to advance the release of effective ...

New

Waters Corporation

AI Architect

Milford, MA · On-site

This includes generative AI, machine learning, automation, and future agent-based capabilities ... and biology. We collaborate with customers around the world to advance the release of effective ...

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Generative Ai Biology information

What is generative AI in biology?

Generative AI in biology refers to the use of artificial intelligence models, particularly generative models like deep learning neural networks, to create new biological data or simulate biological processes. These models can design novel proteins, predict molecular structures, and generate hypotheses for drug discovery. By leveraging large datasets and advanced algorithms, generative AI is accelerating research in genomics, synthetic biology, and personalized medicine. This technology is transforming how scientists approach complex biological problems and is rapidly becoming a vital tool in biotech and pharmaceutical industries.

How do generative AI biology specialists collaborate with interdisciplinary teams?

In Generative AI Biology, collaboration with interdisciplinary teams is crucial for success. Professionals often work closely with computational scientists, bioinformaticians, biologists, and data engineers to design experiments, validate AI-generated predictions, and interpret complex datasets. Regular meetings and collaborative platforms facilitate knowledge sharing, ensuring that AI models are both biologically relevant and technically robust. This dynamic team environment fosters innovation and enables professionals to contribute to both scientific discovery and product-driven applications.

What are the key skills and qualifications needed to thrive as a generative AI biology specialist?

To thrive as a Generative AI Biology specialist, you need a strong foundation in computational biology, machine learning, and life sciences, typically supported by an advanced degree in bioinformatics, computational biology, or a related field. Proficiency in programming languages like Python or R, experience with deep learning frameworks (such as TensorFlow or PyTorch), and familiarity with bioinformatics tools and databases are essential. Strong problem-solving abilities, interdisciplinary communication, and creativity help you bridge gaps between AI technology and biological research. These skills enable you to develop innovative models and solutions that drive advancements in biological discovery and healthcare.

What is the difference between Generative Ai Biology vs Bioinformatics Specialist?

AspectGenerative Ai BiologyBioinformatics Specialist
Required CredentialsDegree in Biology, Computer Science, or related fields; knowledge of AI and machine learningDegree in Bioinformatics, Biology, Computer Science; proficiency in data analysis and programming
Work EnvironmentResearch labs, biotech companies, AI-focused startupsResearch institutions, healthcare, biotech firms, academia
Industry UsageDeveloping AI models to generate biological data, simulate biological processesAnalyzing biological data, developing algorithms for genomics and proteomics

Generative Ai Biology focuses on creating AI models that generate or simulate biological data, combining biology and AI expertise. In contrast, Bioinformatics Specialists analyze biological data using computational tools. Both roles require strong backgrounds in biology and programming, but their core functions differ: one emphasizes AI model development, the other data analysis.

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Generative Ai Biology jobs near you
Infographic showing various Generative Ai Biology job openings in the United States as of August 2026, with employment types broken down into 76% Full Time, 21% Part Time, and 3% Contract. Highlights an 64% Physical, 4% Hybrid, and 32% Remote job distribution.

ML Research Scientist (MLRS) - Generative AI

Achira

San Francisco, CA • On-site

$110 - $150/hr

Other

Re-posted 22 days ago

Job description

About the Role

We’re looking for machine learning researchers who want to shape the frontier of generative models for the atomistic microcosm. You will work at the intersection of cutting‑edge machine learning, statistical mechanics, and approximate Bayesian inference to help us conquer sampling and generation problems at light‑speed. In addition, you’ll collaborate with experts in chemistry and physics to invent and implement models and applications to unlock what’s possible for Achira’s microscopic world models.

While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed. Both locations are offered as hybrid roles, spending at least some of your time working from the office in collaboration with coworkers. Travel is part of all roles at Achira, both to conferences and corporate on‑site activities.

What You’ll Do
  • Invent advanced sampling and simulation methods that integrate probabilistic inference, deep learning, and reinforcement learning to enable efficient exploration and simulation of learned energy landscapes for molecular systems.
  • Design and train frontier generative models: diffusion, autoregressive, flow‑based, and latent‑variable architectures.
  • Build models that can map between data distributions to bridge the gap between simulation and reality.
  • Prototype, benchmark, and iterate rapidly to transform research ideas into reusable and scalable components across Achira’s ecosystem.
  • Collaborate with physicists and chemists to ensure models are grounded in real physics.
  • Work with research engineers and the infrastructure team to identify where research ideas will need support in order to deliver effective results.
About You
  • Interested in building generative models that describe real matter.
  • Drive to build at the frontier of what’s possible and try out new, high‑risk ideas.
  • Machine learning researcher with professional experience (post‑degree) in an industry setting.
  • Demonstrated research impact through conference talks or publications (in machine learning venues), open‑source contributions, or released models.
  • Strong interdisciplinary communication and presentation skills and the ability to translate ideas and concepts to colleagues from non‑ML backgrounds.
  • Proficiency in Python and modern ML frameworks (PyTorch, JAX).
  • Experience collaborating on research projects across multi‑person teams.
  • Desire and comfort with working on frontier problems in physical AI to invent the blueprint for how they will be tackled.
Nice to Have
  • Experience working with models that operate on 3‑D point clouds and dynamic data.
  • Experience in sequential Monte Carlo methods.
  • Experience with probabilistic programming.
  • Experience with pre‑training, mid‑training, and post‑training (especially reinforcement learning) parts of the model development process.
  • Familiarity with statistical mechanics: working knowledge of sampling, estimators, and the Crooks/Jarzynski perspective of nonequilibrium statistical mechanics.
  • Prior experience working in or with researchers in the domains of computational chemistry, biology, or materials science.
  • Experience working with multi‑cloud distributed compute systems.
  • Experience working with multi‑site distributed company team.
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