Design, develop, and deploy AI-driven applications that transform large-scale geospatial data into actionable insights and predictive intelligence
Build and operate end-to-end AI/ML pipelines including data ingestion, preprocessing, feature engineering, training, evaluation, and production inference
Productionize reasoning models, vision-language models (VLMs), and multimodal AI systems that combine imagery, geospatial signals, and structured data
Architect enterprise-grade training and experimentation frameworks, including automated pipelines, experiment tracking, benchmarking, and reproducible evaluation
Create synthetic datasets and test harnesses to validate model performance, robustness, and edge-case behavior in real-world operational environments
Work closely with domain experts, software engineers, product managers, and research partners to translate complex Earth intelligence challenges into deployable AI solutions
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
Stay current with the latest advances in foundation models, generative AI, multimodal learning, and reasoning systems, and translate research breakthroughs into practical systems
Maintain high engineering standards through code reviews, documentation, experimentation discipline, and collaborative problem solving
Help shape the next generation of Earth AI capabilities through collaboration with leading research organizations and technology partners.

Requirements

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
Demonstrated experience designing and delivering end-to-end ML pipelines, including data processing, training automation, evaluation frameworks, and scalable inference
Hands-on experience developing and deploying deep learning models, particularly in one or more of the following areas: Vision-language models (VLMs), Multimodal learning, Reasoning models, Large language models (LLMs), Computer vision or geospatial AI
Strong programming skills in Python, with experience using modern ML frameworks such as PyTorch, TensorFlow, or JAX
Experience building reproducible experimentation pipelines, including model evaluation, dataset versioning, and experiment tracking
Experience deploying models into production environments 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 to bring advanced AI capabilities into real-world applications.

Benefits

robust 401(k) with company match
mental health resources
student loan repayment assistance
adoption reimbursement
pet insurance

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AI-driven applicationsmachine learning systemsend-to-end ML pipelinesdeep learning modelsVision-language modelsMultimodal learningReasoning modelsLarge language modelsPythondata processing

Soft Skills

collaborationproblem solvingcommunicationexperimentation disciplineengineering standards

Certifications

MS in Computer SciencePhD in Computer ScienceMachine LearningArtificial IntelligenceApplied Mathematics