Machine Learning Engineer
Syngenta Group3 days ago
Remote, United StatesSenior
Base Salary
$108k - $200k/yr
Responsibilities
- Design, develop, and deploy production-grade computer vision models for extracting quantitative traits from multimodal imagery and sensor data.
- Build and maintain scalable phenomics pipelines covering image acquisition, preprocessing, trait extraction, quality control, and delivery to downstream data products.
- Collaborate with breeders, researchers, product managers, engineers, and data scientists to translate objectives into validated machine learning solutions.
- Shape the strategic direction of computer vision in phenomics using self-supervised learning and multimodal fusion.
- Manage the full machine learning lifecycle, including problem definition, data exploration, model selection, evaluation, deployment, and monitoring.
- Design and operate cloud-based data pipelines and workflow orchestration systems for imagery and sensor-derived features.
- Productionize research code into maintainable services and pipelines and optimize ML systems for performance, scalability, and reliability.
- Architect and deploy mobile-first AI products that provide real-time image identification, classification, and trait measurements.
- Develop automated image preprocessing and quality-control workflows.
- Share knowledge, document systems, communicate ML concepts to nontechnical stakeholders, and support team learning.
Requirements
- 5+ years of experience in machine learning engineering and data science roles, including 4+ years in applied computer vision.
- Master's or Doctoral degree in Computer Science, Remote Sensing, Engineering, Mathematics/Statistics, Geosciences, or a related technical field is highly desirable.
- Strong foundations in geospatial analysis, image processing, and machine learning.
- Deep expertise in computer vision deep learning architectures, including CNNs, vision transformers, segmentation models, and detection models.
- Experience applying PyTorch, TensorFlow, Keras, scikit-learn, and XGBoost to imagery and other data modalities.
- Strong Python and SQL engineering practices, including packaging, testing, code review, and Git.
- Experience with ML operations tooling, Docker, CI/CD, and one or more of AWS, GCP, or Azure.
- Understanding of data structures, algorithms, statistical methods, and end-to-end modeling, calibration, validation, and application workflows.
- Hands-on experience with ETL/ELT, batch and streaming processing, backfills, idempotency, workflow orchestration, and cloud-native services.
- Experience building and operating ML and data pipelines with Airflow, Argo, Kubeflow, or Prefect.
- Knowledge of plant phenotyping, agricultural applications, or biological imaging in research or commercial environments.
- Knowledge of self-supervised learning, foundation models, transfer learning, and active learning.
- Ability to collaborate across disciplines, communicate complex technical concepts, and work effectively with global teams.
Benefits
- Flexible work options.
- Medical, dental, and vision benefits beginning the first day.
- 401(k) plan with company match, profit sharing, and retirement savings contribution.
- Paid vacation, paid holidays, maternity leave, and paternity leave.
- Education assistance, wellness programs, and corporate discounts.
- Work-life balance, professional development, and a collaborative workplace culture.
Tech Stack
Categories
Data EngineeringML Engineering