Protein Design Scientist, Machine Learning

Syngenta Group

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

Qualifications

  • PhD with over 1 year of experience in bioinformatics, computational biology, or related field focused on protein modeling.
  • Hands-on experience in developing and fine-tuning ML models for protein sequence design.
  • Deep understanding of protein sequence-structure-function relationships.
  • Ability to derive ML/DL hypotheses from complex biological challenges.
  • Excellent communication skills for collaborating with experimental scientists.
  • Proficiency in Python and deep learning libraries (like PyTorch or JAX).

Responsibilities

  • Formulate biological hypotheses and design computational workflows for variant design.
  • Implement and adapt state-of-the-art biomolecular ML models to enhance trait discovery.
  • Collaborate with wet-lab teams to design variant libraries and integrate experimental data.
  • Clearly communicate ML concepts and project updates to stakeholders of varying technical expertise.
  • Stay updated on protein design literature and introduce innovative tools and ideas to the team.

Benefits

  • Culture that promotes collaboration and professional development.
  • Flexible work options to support work-life balance.
  • Comprehensive benefit package starting from day one including medical, dental, and vision.
  • 401k plan with company match and profit sharing.
  • Paid vacation, holidays, and parental leave.
  • Education assistance and wellness programs.
Full 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. Position will be located at Durham, North Carolina with an opportunity for remote work. 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. Syngenta has been ranked as a top employer by Science Journal. Learn more about our team and our mission here: https://www.youtube.com/watch?v=OVCN_51GbNI WL 4B #LI-ONSITE #LI-KR1

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