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Protein Structure Prediction Jobs (NOW HIRING)

RXinsider LTD.

This is a hands‑on computational protein design role for a scientist who can apply modern ... Use structure prediction, generative design, sequence design, docking, scoring, and developability ...

RXinsider LTD.

Scientist, Protein Design

Cambridge, MA · On-site

This is a hands‑on computational protein design role for a scientist who can apply modern ... Use structure prediction, generative design, sequence design, docking, scoring, and developability ...

Battelle

Computational Biologist

Columbus, OH · On-site

Structure prediction, refinement, and conformational analysis * Molecular dynamics simulations and structure based optimization * Integration of AI/ML tools for protein design and optimization

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Protein Structure Prediction information

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How much do protein structure prediction jobs pay per hour?

As of Sep 11, 2026, the average hourly pay for protein structure prediction in the United States is $33.06, according to ZipRecruiter salary data. Most workers in this role earn between $21.63 and $40.87 per hour, depending on experience, location, and employer.

What is protein structure prediction?

Protein structure prediction is the process of determining the three-dimensional shape of a protein from its amino acid sequence using computational methods. This is important because a protein’s function is largely determined by its structure, but experimental methods like X-ray crystallography and cryo-EM can be time-consuming and costly. Recent advances in artificial intelligence, such as AlphaFold, have significantly improved the accuracy and accessibility of protein structure prediction, making it an essential tool in biological research and drug discovery.

What are the key skills and qualifications needed to thrive in protein structure prediction, and why are they important?

To thrive in protein structure prediction, you need a strong background in bioinformatics, molecular biology, and computational modeling, often supported by an advanced degree in a related field. Familiarity with tools such as AlphaFold, Rosetta, PyMOL, and experience with programming languages like Python or R are typically required. Analytical thinking, problem-solving abilities, and effective collaboration are crucial soft skills for interpreting data and working in interdisciplinary teams. These skills enable accurate modeling of protein structures, which is vital for advancements in drug discovery and understanding biological processes.

What are some common challenges faced by professionals working in protein structure prediction roles?

One of the main challenges in protein structure prediction is managing the complexity and variability of protein folding, which often requires the integration of large-scale datasets and advanced computational methods. Professionals must also stay current with rapidly evolving algorithms and software tools, and may need to troubleshoot issues related to limited experimental data or ambiguous sequence information. Collaboration with biologists, chemists, and data scientists is frequent, as teams work together to validate predictions and interpret results for downstream applications such as drug discovery.

What other helpful pages are available for Protein Structure Prediction?

Other pages related to Protein Structure Prediction:

Infographic showing various Protein Structure Prediction job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 53% Physical, 2% Hybrid, and 45% Remote job distribution, with an average salary of $68,765 per year, or $33.1 per hour.

Member of Technical Staff, Protein Design

San Francisco, CA • On-site

Radical Numerics, Inc
See more jobs at Radical Numerics, Incexternal

Full-time

Re-posted 21 days ago

Key responsibilities

  • Develop and improve machine-learning models for protein structure prediction, design, and related structural biology tasks.

  • Train and fine-tune protein language models, geometric neural networks, diffusion models, and other modern scientific machine-learning architectures.

  • Build reliable data pipelines and evaluation systems for structural modeling.

Job description

About Us
Radical Numerics is an AI research lab building general biological intelligence. Our mission is to master the code of life, and our purpose is to reduce human suffering.
Our team created Evo, and started the field of generative genomics. Our work was featured on the cover of Science, and presented by our CEO on the main stage of TED2025. Evo was used to create the first AI gene therapy tool CRISPR-Cas9, and the first AI whole genome from scratch. Evo 2, featured in Nature, is the largest fully open source AI project across any domain.
Radical Numerics is bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We've redesigned the foundation model training stack to turn the world's raw scientific data (e.g. biological sequences, experiments, and physical processes), into intelligible, generative models that can expand and accelerate what humanity can understand, design, and cure.
The same generative breakthroughs that enable life-saving cures also lowers the barrier to creating engineered threats and AI-generated bioweapons. We believe these forces are inseparable. Radical Numerics was founded to develop both the power to design and the responsibility to defend.
About the Role
As a Member of Technical Staff, Protein Design, you will develop advanced machine-learning systems at the frontier of molecular modeling,from protein language models to structure prediction and beyond.
You will work at the intersection of large-scale biological models, geometric deep learning, and structural biology. The role spans model development, training, evaluation, and scientific analysis, with a strong emphasis on building systems that generalize beyond standard benchmarks.
This is a hands-on research and engineering role. You will be expected to implement models, run large-scale experiments, diagnose failure modes, and develop rigorous ways to evaluate scientific performance. You will collaborate closely with researchers across machine learning, computational biology, and biological modeling.
What You'll Do
  • Develop and improve machine-learning models for protein structure prediction, design, and related structural biology tasks.
  • Train and fine-tune protein language models, geometric neural networks, diffusion models, and other modern scientific machine-learning architectures.
  • Explore new architectures and learning objectives for modeling protein sequence and structure.
  • Build reliable data pipelines and evaluation systems for structural modeling.
  • Design rigorous benchmarks that measure generalization and minimize data leakage or memorization.
  • Evaluate models using established structural accuracy, confidence, and physical-validity metrics.
  • Analyze model performance across diverse proteins, structural classes, and biological contexts.
  • Run ablation studies and controlled experiments to understand the impact of model architecture, data, scale, and training methodology.
  • Improve the efficiency and reliability of model training and inference on large-scale compute systems.
  • Collaborate with scientists and engineers to translate research advances into robust modeling capabilities.

What We're Looking For
  • Strong experience developing machine-learning models for protein structure prediction, structural biology, geometric deep learning, or a closely related area.
  • Experience training or fine-tuning protein language models, structure models, diffusion models, or other large scientific machine-learning systems.
  • Deep understanding of modern protein structure-prediction and design methods, loss objectives, and architectures.
  • Familiarity with geometric neural networks, equivariant architectures, pairwise representations, and generative modeling of molecular structure.
  • Strong knowledge of protein structure, including secondary and tertiary structure, protein domains, complexes, conformational flexibility, and evolutionary constraints.
  • Experience building and validating biological datasets and controlling for data leakage, homology, and benchmark contamination.
  • Familiarity with commonly used protein structure metrics and evaluation practices.
  • Fluency in Python and a modern deep-learning framework such as PyTorch or JAX.
  • Experience with distributed training, accelerators, large datasets, and reproducible experimentation.
  • Strong experimental judgment and the ability to distinguish genuine scientific progress from benchmark artifacts.
  • Ability to independently move between research, implementation, experimentation, and scientific analysis.
  • Clear communication skills and an ability to collaborate across machine learning, computational biology, and engineering.

Expertise in machine learning, computational biology, structural biology, biophysics, computer science, or a related field, or an equivalent record of research and engineering impact.
Nice to Have
  • Contributions to protein structure-prediction systems, protein foundation models, geometric generative models, or widely used structural biology software.
  • Familiarity with multiple sequence alignments, templates, coevolutionary methods, inverse folding, molecular simulation, or energy-based modeling.
  • Experience modeling other macromolecules, small molecule systems, and molecular interactions.
  • Understanding of over major protein structure datasets, benchmarks, or community evaluation efforts.
  • Experience modeling protein complexes or alternative conformational states.
  • Experience with SE(3)- or E(3)-equivariant architectures, diffusion models, flow matching, or generative modeling of molecular coordinates.
  • Experience evaluating model confidence, uncertainty, and calibration.
  • Familiarity with experimental methods for determining protein structure.
  • A record of publications, open-source contributions, or production systems demonstrating impact in generative modeling, molecular design, or scientific machine learning.

Radical Numerics is committed to equal employment opportunity and does not discriminate in any employment opportunities or practices based on an individual's race, color, creed, gender (including gender identity and gender expression), religion (all aspects of religious beliefs, observance or practice, including religious dress or grooming practices), marital status, registered domestic partner status, age, national origin or ancestry (including language use restrictions and possession of a driver's license issued under California Vehicle Code section 12801.9), natural hair, physical or mental disability, political affiliation, medical condition (including cancer or a record or history of cancer, and genetic characteristics), sex (including pregnancy, childbirth, breastfeeding or related medical condition), genetic information, sexual orientation, military and veteran status or any other consideration made unlawful by federal, state, or local laws. It also prohibits unlawful discrimination based on the perception that anyone has any of those characteristics, or is associated with a person who has or is perceived as having any of those characteristics.
Radical Numerics participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S.

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