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Mid-level, metro role but niche geospatial ML reduces applicant density.
Highly domain-specific geospatial computer-vision and foundation-model expertise limits cross-industry transferability.
Requires 5+ years, peer-reviewed publications, production ML experience and demonstrated technical leadership.
Lead and set technical direction for a team of 4-5 scientists building ML systems to interpret satellite imagery at scale for agriculture, forestry, and environmental monitoring.
Own end-to-end delivery of large-scale ML systems including problem framing, data design, model development, deployment, and monitoring.
Mentor junior scientists and ensure research translates into reliable, scalable production systems while collaborating across teams to convert ambiguous requirements into technical plans.
5+ years applied ML/Computer Vision experience or PhD with 3+ years post-degree experience in CS, EE, or related field.
Deep expertise in semantic/instance segmentation, object detection, encoder-decoder and transformer architectures, temporal/sequential modelling.
Proven experience leading small technical teams and mentoring researchers, with track record of peer-reviewed publications or patents.
Proficiency in PyTorch and Python with strong software engineering skills; familiarity with distributed training and MLOps tooling.
Experienced in research and production deployment of ML models for geospatial or remote sensing data, preferably satellite imagery.
Capable of strategic technical leadership balancing hands-on research with team mentorship and roadmap planning.
Skilled at designing experimental frameworks and setting team standards for evaluation, reproducibility, and documentation.