Principal AI Engineer, Machine Learning Operations
New
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BlackSkyGeospatial intelligence
Location: Remote, USAFull-TimePrincipal
Salary185,000 - 215,000 USD per year
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Job Details
- Experience
- Minimum of 12 years of hands-on software engineering experience, including at least four years developing computer vision or machine learning solutions for geospatial, remote sensing, or similarly complex imagery. Preferred: At least 15 years of hands-on software engineering, computer vision, or machine learning experience.
- Required Skills
- AWSPythonMachine LearningPyTorchMLOpsComputer Vision
Requirements
- Have at least 12 years of hands-on software engineering experience, including at least four years developing computer vision or machine learning solutions for geospatial, remote-sensing, or similarly complex imagery.
- Hold a bachelor's degree in computer science, engineering, mathematics, or a related quantitative field, or have equivalent practical experience.
- Demonstrate expert-level proficiency in Python and experience developing deep learning systems with PyTorch or a comparable modern ML framework.
- Have experience designing, training, evaluating, and deploying production-grade computer vision models for tasks such as object detection, segmentation, change detection, image classification, or time-series analysis.
- Have strong experience with remote-sensing imagery and geospatial data challenges, including spatial resolutions, viewing geometries, sensors, and environmental conditions.
- Have hands-on experience building geospatial data and computer vision pipelines using tools such as Rasterio, GDAL, GeoPandas, Shapely, xarray, or Zarr.
- Have experience taking computer vision capabilities from experimentation through production, including model evaluation, deployment, and performance optimization.
- Have experience developing and operating machine learning workloads in AWS, including GPU-based training and inference.
- Be able to communicate architecture decisions, experimental results, tradeoffs, risks, and recommendations to technical and non-technical stakeholders.
- Demonstrate technical leadership and mentorship across engineering teams without requiring direct management authority.
Responsibilities
- Lead full-lifecycle model engineering, from problem formulation and dataset curation through training, validation, deployment, and error analysis.
- Drive model resilience across satellite imagery conditions, including off-nadir angles, low light, seasonal variation, weather distortion, dense environments, and cross-sensor domain shifts.
- Ensure models are trained, versioned, deployed, monitored, and maintained reliably in production.
- Optimize edge and cloud compute environments for resilient capabilities.
- Communicate technical progress, risks, and technology stack to cross-functional leadership and Product partners.
- Contribute to proposals and white papers.
- Provide technical mentorship and lead design reviews and experiment planning.
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