Machine Learning Lead

Machine Learning Lead

https://github.com/Greenstand/treetracker-machine-learning

Commitment:

24-months

Role Overview:

The Machine Learning Lead is responsible for guiding the development, deployment, and optimization of Greenstand’s machine learning systems. This includes overseeing image analysis processes for tree species identification, disease detection, image quality assessment, and tree growth tracking. The role involves providing technical leadership to contributors, ensuring model scalability, and aligning machine learning initiatives with Greenstand’s mission and data infrastructure.

Compensation:

Volunteer, until otherwise funded

Reports to:

Machine Learning Lead

Collaborates with:

Contributors, System users, Governmental agencies

Leads

Machine Learning

AREAS of RESPONSIBILITY

• Provide technical leadership in the design, training, deployment and optimisation of ML models used across Greenstand’s platforms.
• Oversee integration of ML models into production environments, ensuring reliability, scalability and performance.
• Lead optimisation and maintenance of workflow pipelines (e.g., via Apache Airflow) for automated species tagging, image processing and model retraining.
• Ensure model version control and notebook & dataset management via GitHub (fork-merge workflows, notebook uploads, etc). GitHub
• Mentor contributors and guide collaborative ML development following open-source best practices (e.g., repository contributions, issues, PRs). GitHub
• Collaborate with backend engineers to ensure efficient API endpoints and database integration for serving ML services.
• Establish and maintain evaluation protocols to ensure model accuracy and continuous improvement (species tagging, disease detection, duplicate-image detection, growth tracking) as identified in the repository goals. GitHub
• Document workflows, processes and model performance metrics; maintain clear versioning and reproducibility.
• Coordinate cross-team communication using Slack, project-tracking tools and repository Projects boards. GitHub
• Identify and explore new machine-learning opportunities that strengthen ecosystem-restoration monitoring and reporting (for example tree growth & update tracking, image-quality analysis). GitHub

QUALIFICATIONS/EXPERIENCE/KNOWLEDGE:

• Proven experience leading ML projects from research to production deployment.
• Strong proficiency in Python and ML frameworks such as TensorFlow, PyTorch or similar.
• Experience with workflow orchestration tools like Apache Airflow.
• Understanding of model deployment on cloud platforms (AWS, GCP or Azure).
• Familiarity with containerisation and CI/CD tools (Docker, Kubernetes, GitHub Actions).
• Excellent problem-solving and debugging skills.
• Strong communication and mentorship abilities in a remote, asynchronous environment.
• Background in computer-vision, image-processing or geospatial data analysis is an advantage.
• Comfortable working within an open-source and collaborative repository environment, managing forks, pull requests and notebook sharing.

Next Steps...

Step 1: Complete Our Volunteer Registration Form: Tell us about your skills, goals, and volunteer expectations. - https://docs.google.com/forms/d/e/1FAIpQLSeEmwekGZ5tcc5sVhsgMDf-KEDBMtZYoCYAmxyxrOPv5w0WKA/viewform

Step 2: Join Greenstand Slack: Introduce yourself in the hashtag#community-intros channel and indicate the specific role you are interested in. We can’t wait to meet you! - https://join.slack.com/t/greenstand/shared_invite/zt-2ybd2u95j-WMHOPSSFd6_tjw034Wl5xQ

Step 3: Schedule Your Orientation: Wait for a Volunteer Onboarding Coordinator who will help you get involved in the community!

We look forward to making the world a greener place with you!

Sincerely,

The Greenstand Team