Machine Learning Engineer - Plant Genomics AI at Boyce Thompson Institute for Plant Research – Ithaca, New York

Machine Learning Engineer - Plant Genomics AI

Buckler Laboratory, Boyce Thompson Institute

Position Overview

The Buckler Lab at the Boyce Thompson Institute (BTI) seeks two skilled Machine Learning Engineers to advance our AI research initiatives in plant genetics and genomics. Our lab, based at BTI, Cornell, and USDA-ARS, conducts cutting-edge research working to address three main questions: How can we use genetics to make agriculture more efficient and share those efficiencies globally? How can we reduce the impact of agriculture on the environment? How does genetic variation give rise to phenotypic variation?

Our team develops and maintains sophisticated AI tools and software for genomic analysis and data management, serving our research laboratory, plant breeding programs, and the global genetics research community.

Key Responsibilities

• Design, train, and evaluate machine learning models for diverse plant genetics applications
• Deploy production-ready ML models and maintain model performance in operational environments
• Stay current with state-of-the-art ML methodologies, frameworks, and research developments
• Support multiple concurrent machine learning projects across various research domains
• Collaborate with researchers to translate biological questions into ML problem formulations
• Create compelling data visualizations and communicate results to technical and non-technical stakeholders
• Contribute to research publications and present findings at scientific conferences

Optimize model performance and computational efficiency for large-scale genomic datasets

What We Offer

Join a world-class research team where your ML expertise will drive breakthrough discoveries in plant genetics and contribute to global food security solutions. Work at the intersection of cutting-edge AI technology and impactful biological research.

Salary Range - $85,000 - $109,000 (within range determined by experience and/or advanced degree)

Remote work option not available, must work onsite in Ithaca, NY

Required Qualifications

• Bachelor's degree in Computer Science, Machine Learning, Bioinformatics, or related field
• 2-4 years of hands-on experience training and deploying machine learning models
• Demonstrated proficiency with GPU computing for ML applications
• Expert-level Python programming skills
• Extensive experience with modern ML frameworks (PyTorch, HuggingFace, NumPy, scikit-learn)
• Experience with data preprocessing, feature engineering, and statistical analysis methods for biological data
• Knowledge of deep learning architectures (CNNs, RNNs, Transformers, etc.)
• Proficiency in model evaluation and validation techniques (cross-validation, performance metrics, bias detection)
• Experience with probability and/or applied mathematics, especially with respect to ML/AI modeling
• Experience handling large datasets and data pipeline development
• Proven ability to create effective data visualizations and technical reports
• Strong version control skills using Git
• Experience with Agile development methodologies and collaborative workflows
• Proficiency in Linux environments
• Excellent written and verbal communication skills with ability to explain complex concepts
• Strong organizational and project management capabilities
• Demonstrated success working in interdisciplinary team environments
• Commitment to staying current with rapidly evolving ML landscape

Preferred Qualifications

• Advanced degree (MS/PhD) in relevant field
• Experience with additional programming languages (Java, Kotlin, C/C++)
• Experience with biological/genomic data formats (FASTA, VCF, BAM, etc.)
• Background in computational biology, bioinformatics, or genomics
• Experience with cloud computing platforms (AWS, Google Cloud, Azure)
• Familiarity with MLOps practices and model deployment pipelines
• Knowledge of statistical genetics or quantitative genetics
• Experience with distributed computing frameworks
• Publications in machine learning or computational biology