Machine Learning Engineer - Plant Genomics AI at Boyce Thompson Institute for Plant Research | Findjobs | Findjobs
Machine Learning Engineer - Plant Genomics AI
Boyce Thompson Institute for Plant Research
Location
🇺🇸 United States
Type
Full-time
Salary
$7.1k–$9.1k
Posted
3d ago
Job Description
Description 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
More Machine Learning Engineer Roles in United States
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
Requirements
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