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Generative Ai Biology Jobs (NOW HIRING)

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 ...

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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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Infographic showing various Generative Ai Biology job openings in the United States as of September 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Postdoctoral Fellow, Structural Biology

Buffalo, NY • On-site

University at Buffalo
Colleges, Universities, and Professional Schools • 5 - 10K employees
Learn more about University at Buffaloexternal

$70K - $85K/yr

Full-time

Re-posted 7 days ago

University at Buffalo rating

7.2

Company rating: 7.2 out of 10

Based on 28 frontline employees who took The Breakroom Quiz

Job description

Posting Details
Position Information
Fiscal Year
2025-2026
Position Title
Postdoctoral Fellow, Structural Biology
Classification Title
Postdoctoral Associate
Department
Structural Biology Department
Posting Number
R260058
Posting Link
https://www.ubjobs.buffalo.edu/postings/61964
Employer
Research Foundation
Position Type
RF Professional
Job Type
Full-Time
Appointment Term
Salary Grade
E.89
Posting Detail Information
Position Summary
The Grant Lab at the University at Buffalo is seeking a highly skilled Postdoctoral Fellowto lead the computational development of a novel generative AI framework for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein ensemble structures. As an Empire AI-funded fellow, you will have early access to the Empire AI clusters, utilizing state-of-the-art GPU architectures to push the boundaries of structural biology.
This position is a prestigious Empire AI Fellowship at the University at Buffalo, designed for a "CS-first" researcher to drive the technical evolution and large-scale implementation of a new platform for protein structure prediction. Working at the intersection of generative AI and biophysics, the Fellow will focus on expanding the current framework to model dynamic protein ensembles. As an Empire AI-funded fellow, you will have access to the Empire AI Alpha and Beta clusters, utilizing hundreds of state-of-the-art GPUs (including H100 and GB200 nodes) for scaling generative models for structural biology.
The Postdoctoral Fellow will be responsible for:
  • Technical Expansion: Implementing the next phase of the project to transition from rigid-body models to sophisticated systems for protein ensemble modeling.
  • Computational Optimization: Resolving hardware-specific performance and numerical precision challenges across diverse GPU environments.
  • Architecture Design: Leading the design of new loss functions, model architectures, and synthetic datasets that integrate experimental X-ray scattering data.
  • System Stability: Ensuring numerical reproducibility and stability in large-scale distributed training workloads.

Learn more:
  • Our benefits, where we prioritize your well-being and success to enhance every aspect of your life.
  • Being a part of the University at Buffalo community.

As an Equal Opportunity / Affirmative Action employer, the Research Foundation will not discriminate in its employment practices due to an applicant's race, color, religion, sex, sexual orientation, gender identity, national origin and veteran or disability status.
Minimum Qualifications
  • Doctoral degree or equivalent in Computer Science, Data Science, Computational Physics, or a related field with a focus on Deep Learning.
  • All degree requirements, including dissertation, must be completed by the start date.
  • Expert-level proficiency in PyTorch and/or JAX.
  • Demonstrated experience with Distributed Training (e.g., DeepSpeed, FSDP) and managing large-scale GPU workloads.
  • Strong understanding of low-level model stability, including mixed-precision training (BF16/FP8) and gradient accumulation.

Preferred Qualifications
  • At least two years of experience beyond the PhD in a research or engineering environment focused on large-scale AI.
  • Experience with geometric deep learning, diffusion architectures, or related frameworks (e.g., OpenFold, AlphaFold2/3).
  • Familiarity with Docker/Singularity for reproducible HPC environments.
  • Experience with CUDA-level optimization or debugging hardware-specific performance differences.
  • Basic knowledge of protein structure, folding, or biophysics.

Our University Community
Physical Demands
Driving Requirements
Salary Range
$70,000 - $85,000
Additional Salary Information
The salary range reflects our good faith and reasonable estimate of the possible compensation at the time of posting, the role and associated responsibilities, and the experience, education, and training of the selected candidate.
Work Hours
37.5 hours per week
Campus
Downtown Campus
Posting Alerts
Special Instructions Summary
Is a background check required for this posting?
No
Background Check Notification
Contact Information
Contact's Name
Thomas Grant
Contact's Pronouns
Contact's Title
Assistant Professor
Contact's Email
tdgrant@buffalo.edu
Contact's Phone
716-829-5490
Posting Dates
Posted
04/10/2026
Deadline for applicants
Open Until Filled
Date to be filled
References
Number of references required
3
Reference Cutoff Date
Instructions to Applicant

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