Machine Learning Engineer

Overview

Machine Learning Engineer to develop and deploy production-grade computer vision and ML solutions for agriculture, with an initial focus on imagery and sensor data for breeding programs.

What you'll do

• Design, develop, and deploy computer vision models that extract quantitative digital traits from multi-modal imagery and sensor data.
• Build and maintain scalable phenomics pipelines to process field plots and deliver downstream data products.
• Collaborate with stakeholders to translate objectives into ML solutions and validate outputs against ground truth.
• Own cloud-based data pipelines and workflow orchestration for ingesting, validating, transforming, and delivering features.
• Productionalize research code into maintainable services and optimize ML systems for performance, scalability, and reliability.
• Architect and deploy mobile-first AI products for image capture and real-time identification/classification/trait measurements.

What you'll need

• 5+ years of experience in machine learning engineering and data science roles.
• 4+ years in applied computer vision.
• Deep expertise in deep learning architectures for computer vision and experience with ML frameworks (PyTorch, TensorFlow, Keras, scikit-learn, XGBoost).
• Strong Python and SQL engineering practices for productionalizing ML models.
• Experience with MLOps tooling (e.g., MLflow, Weights & Biases), Docker, CI/CD, and one or more cloud platforms (AWS, GCP, Azure).
• Hands-on experience building and operating ML/data pipelines using workflow orchestrators (e.g., Airflow/Argo/Kubeflow/Prefect) and cloud-native services.
• Domain knowledge related to plant phenotyping, agricultural applications, or biological imaging.

Details

• Work mode: Remote (NC, United States location listed).
• Residency/authorization: Must reside in and be permanently authorized to work in the United States without future employer sponsorship.
• Salary range: $107,800 to $200,200 annually.