Protein Design Scientist

Syngenta Group

$110K — $130K *
Pharmaceuticals & Biotech
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • PhD with +1 year in bioinformatics, computational biology, or related field focusing on protein modeling.
  • Hands-on experience developing ML models for protein sequence design and property prediction.
  • Deep understanding of protein sequence-structure-function relationships.
  • Ability to transform complex biological challenges into actionable ML hypotheses.
  • Strong communication skills for collaborating with wet-lab scientists.
  • Experience with Python and deep learning libraries such as PyTorch or JAX.

Responsibilities

  • Formulate biological hypotheses and design workflows for variant design and trait discovery.
  • Implement and tune advanced biomolecular ML models to drive trait pipeline innovation.
  • Partner with wet-lab teams to design variant libraries and integrate experimental data.
  • Communicate deep learning concepts and project progress to diverse stakeholders.
  • Stay updated with protein design literature to introduce new tools and ideas.

Benefits

  • Culture promoting belonging and collaboration with a focus on professional development and work-life balance.
  • Comprehensive benefit package including Medical, Dental & Vision starting on day one.
  • 401k plan with company match, and profit-sharing contributions.
  • Paid Vacation, Holidays, Maternity and Paternity Leave, and Education Assistance.
  • Wellness Programs and Corporate Discounts.
Full Job Description
Job Description

As a Protein Design Scientist, you leverage an AI-first approach, utilizing protein language models (pLMs) and generative sequence design to explore sequence-function relationships and pioneer next-generation agricultural traits. As an Applied ML Scientist, you are a hypothesis-driven scientist who leverages and adapts open-source machine learning models to biological data, addressing complex biological questions where data may be sparse and expensive to generate. You are also a collaborative team player who thrives in the dry-to-wet lab loop by turning agricultural and trait challenges into practical machine learning hypotheses and projects, while translating complex ML concepts and outputs into clear, practical suggestions for diverse stakeholders.

Accountabilities:

  • Design & Optimize: Formulate biological hypotheses and design computational workflows for large scale variant design and property prediction to accelerate trait discovery
  • Deploy ML Models: Implement, adapt, and tune state-of-the-art biomolecular ML models-including single-sequence LMs, generative models, co-evolutionary aware architectures, and 3D structural prediction models-to drive innovative projects for the trait pipeline
  • Collaborate Cross-Functionally: Partner closely with wet-lab research teams to design variant libraries, leveraging active learning and Bayesian optimization to iteratively integrate experimental screening data into design loops
  • Communicate Insights: Communicate complex deep learning concepts, protocols, and project progress clearly to technical and non-technical stakeholders
  • Innovate: Monitor the rapidly changing protein design literature and bring promising new tools and project ideas to the team


Qualifications
  • PhD with +1-year experience in bioinformatics, computational biology, biochemistry & biophysics, or a related field with a focus on protein sequence or structure modeling
  • ML and pLM Experience: Demonstrated hands-on experience developing, adapting, or fine-tuning machine learning models for protein sequence design, variant library generation, protein property prediction, or related biomolecular engineering tasks
  • Protein Science Expertise: Deep understanding of protein sequence-structure-function relationships
  • Scientific Problem Solving: Proven ability to transform complex biological challenges into actionable, ML/DL computational hypotheses to support the trait pipeline
  • Collaborative Communication: Strong communication skills with a track record of working effectively alongside experimental/wet-lab scientists
  • ML & Deep Learning Engineering: Experience working with Python and deep learning libraries (like PyTorch or JAX) to adapt or fine-tune open-source models

Desired Qualifications:
  • Structural Integration and biophysics: Experience integrating protein structural information, including structure prediction models (AlphaFold, Boltz), structure-aware design methods (ProteinMPNN, GNNs, structure-conditioned pLMs, Foldseek 3Di), or physics-based approaches (Rosetta, molecular dynamics) to augment sequence-based design and prediction workflows.
  • Wet-Lab History: Strong familiarity of or experience in wet-lab workflows-either in library design or generating screening data for model training or validating designed variants (e.g., protein characterization)-to facilitate seamless communication with experimental partners
  • Compute and data: Familiarity querying large-scale biological datasets, working in cloud environments (AWS, HPC), and utilizing containerized workflows (Docker, Singularity, Nextflow)
  • Industry Experience: Prior experience in agricultural biotechnology, plant biology, or a strong interest in translating computational protein design to crop science


Additional Information

What We Offer:
  • A culture that celebrates belonging and collaboration, promotes professional development and strives for a work-life balance that supports the team members. Offers flexible work options to support your work and personal needs.
  • Full Benefit Package (Medical, Dental & Vision) that starts your first day.
  • 401k plan with company match, Profit Sharing & Retirement Savings Contribution.
  • Paid Vacation, Paid Holidays, Maternity and Paternity Leave, Education Assistance, Wellness Programs, Corporate Discounts, among other benefits.


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