Computational Biologist – Deep Learning at Syngenta Group

Durham, North Carolina, United States -

Full Time

Start Date

Immediate

Expiry Date

14 May, 26

Salary

0.0

Posted On

13 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Deep Learning, Bioinformatics, Genomics, Computational Biology, AI Models, Proteomics, Epigenetics, Machine Learning, Data QC, Statistical Analysis, Model Architectures, Python, PyTorch, TensorFlow, Keras, JAX

Industry

Farming

Description

Company Description Syngenta Seeds is one of the world’s largest developers and producers of seed for farmers, commercial growers, retailers and small seed companies. Syngenta seeds improve the quality and yields of crops. High-quality seeds ensure better and more productive crops, which is why farmers invest in them. Advanced seeds help mitigate risks such as disease and drought and allow farmers to grow food using less land, less water and fewer inputs. Syngenta Seeds brings farmers more vigorous, stronger, resistant plants, including innovative hybrid varieties and biotech crops that can thrive even in challenging growing conditions. Syngenta Seeds is headquartered in the United States. Job Description At Syngenta, we believe every employee has a role to play in safely feeding the world and taking care of our planet. To support that challenge, the Bioinformatics group is seeking a Computational Biologist to help design, build, and deploy Artificial Intelligence models (e.g., deep learning) to analyze genomics data for crop improvement. The position is in Durham, North Carolina. Role Purpose: Work in a team of computational biologists to train, modify and apply deep learning models, and to create or enhance workflows that leverage these models in our seeds pipeline Join cross-functional diverse research teams to deliver the next generation of predictive and generative technologies to support Syngenta Seed’s product pipeline Become an active member of the Bioinformatics group and drive innovation in a rapidly evolving scientific discipline Synthesize results and clearly communicate progress and challenges to project team members Accountabilities: Assist in the design, building, and implementation of advanced predictive AI models using genomics data (gene expression, proteomics, epigenetics) Data preparation for internal machine learning and deep learning (ML/DL) projects, including data QC, basic statistical analysis, preprocessing, etc. Identification and organization of public and internal data sources for DL, based on specified use cases Exploration and implementation of cutting-edge model architectures available in the public domain Modification of existing DL model architectures to support new use cases Development of workflows that utilize DL models to support cross-functional project teams Collaborate with cross-functional stakeholders to accelerate AI adoption in product pipelines by explaining model predictions, validating biological relevance, and demonstrating measurable improvements in trait prediction and candidate selection Prepare thorough documentation of model architectures, experimental findings, and validation metrics and deliver presentations to diverse stakeholders, including data scientists and biologists Qualifications PLEASE NOTE -- Candidates must be already located in the United States and not require visa sponsorship now or in the future (Includes OPT). Required: PhD or MS with experience in Deep Learning, Bioinformatics, Genomics, Computational Biology, or closely related area Strong understanding of plant or plant-pest/pathogen biology, gene regulation, and genomics Applicants must be familiar with LLM concepts and popular LLM architectures Excellent communication skills and the ability to work in a highly dynamic and collaborative environment Technical skills: Experience working with biological and multi-omics data Proficiency in the use of UNIX/Linux and its command-line environment Proficiency coding in Python Experience with PyTorch, TensorFlow/Keras,or JAX Experience with deep learning model creation, fine-tuning, hyperparameter optimization, and model deployment Knowledge/experiences in using LLMs (Transformers, Mamba, etc) Proficiency in grid computing (e.g. Univa, Slurm, etc.) Familiarity working with version control systems (e.g. Git, CVS, SVN,etc) Experience with Jupyter notebooks and conda environments Desired: Experience with MLOps tools (e.g. MLflow) Experience with cloud computing with AWS knowledge Experience building/fine-tuning LLMs (transformers, Mamba, Hyena, etc) Experience in programming with Github-Copilot, AWS Q, or similar AI agents to enable rapid development Additional Information What We Offer: A culture that celebrates diversity & inclusion, 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 Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status. WL: 4B #LI-Hybrid

Responsibilities

The role involves working within a computational biology team to train, modify, and apply deep learning models, and to create workflows leveraging these models for the seeds pipeline. Responsibilities include assisting in the design and implementation of advanced predictive AI models using genomics data and preparing data for internal ML/DL projects.