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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and deploying AI-driven applications, particularly in machine learning and geospatial data processing. Proficient in building end-to-end ML pipelines and optimizing models for performance and scalability on cloud infrastructure.
Highest-signal resume keywords
Machine Learning Systems DeploymentEnd-to-End ML Pipeline DesignDeep Learning Model DevelopmentPython ProgrammingExperience with PyTorch or TensorFlow
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningDeep LearningGeospatial AIModel OptimizationData IngestionFeature EngineeringModel EvaluationExperiment TrackingSynthetic Dataset CreationMultimodal Learning
Soft Skills
CollaborationProblem SolvingCommunication
Tools & Technologies
Google Cloud PlatformPyTorchTensorFlowJAXContainerized Systems
Industry Keywords
Geospatial DataRemote SensingSatellite ImageryEarth Observation SystemsFoundation Models
Tech Stack
Tools & technologiesCloudGoogle Cloud PlatformPythonPyTorchRemote SensingTensorflow
About the role
Key responsibilities & impact- Design, develop, and deploy AI-driven applications transforming large-scale geospatial data into actionable insights and predictive intelligence
- Build and operate end-to-end AI/ML pipelines covering data ingestion, preprocessing, feature engineering, training, evaluation, and production inference
- Productionize reasoning models, vision-language models, and multimodal AI systems combining imagery, geospatial signals, and structured data
- Architect enterprise-grade training and experimentation frameworks with automated pipelines, experiment tracking, benchmarking, and reproducible evaluation
- Create synthetic datasets and test harnesses to validate model performance, robustness, and edge-case behavior
- Collaborate with domain experts, software engineers, product managers, research partners, and technology partners
- Optimize models and inference systems for scalability, latency, cost efficiency, and reliability on modern cloud infrastructure
- Implement and maintain production inference systems, including monitoring, model versioning, retraining workflows, and performance tracking
- Track advances in foundation models, generative AI, multimodal learning, and reasoning systems and translate them into practical systems
- Maintain engineering standards through code reviews, documentation, experimentation discipline, and collaborative problem solving
- Help shape next-generation Earth AI capabilities
Requirements
What you’ll need- U.S. Person status required: U.S. citizen, permanent resident, Asylee, or Refugee
- MS or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, or a related technical field, or equivalent practical experience
- 5+ years of experience building and deploying machine learning systems in production environments
- Experience designing and delivering end-to-end ML pipelines
- Experience developing and deploying deep learning models
- Experience with vision-language models, multimodal learning, reasoning models, large language models, computer vision, or geospatial AI
- Strong Python programming skills
- Experience with PyTorch, TensorFlow, or JAX
- Experience building reproducible experimentation pipelines
- Experience deploying models using modern cloud infrastructure and containerized systems
- Familiarity with distributed training, large-scale data processing, and model optimization techniques
- Ability to collaborate across research, engineering, and product teams
- Typically requires 8 years of related experience with a Bachelor's degree; or 6 years with a Master's degree; or a PhD with 3 years experience; or equivalent experience
- Preferred: experience with geospatial data, remote sensing, satellite imagery, or Earth observation systems
- Preferred: experience building or fine-tuning foundation models, multimodal models, or agentic AI systems
- Preferred: familiarity with Google Cloud Platform (GCP)
- Preferred: experience implementing model monitoring, evaluation pipelines, and automated retraining systems
- Preferred: contributions to open-source AI projects, research publications, or patents
Benefits
Comp & perks- Robust 401(k) with company match
- Mental health resources
- Student loan repayment assistance
- Adoption reimbursement
- Pet insurance
- Incentive eligible with a target based on contribution, company performance, and/or individual results achieved