Responsibilities 

• 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. 

Minimum Qualifications 

• 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. 

Preferred Qualifications 

• Experience working with geospatial data, remote sensing, satellite imagery, or Earth observation systems. 

• Experience building or fine-tuning foundation models, multimodal models, or agentic AI systems. 

• Familiarity with Google Cloud Platform (GCP), including large-scale AI/ML infrastructure. 

• Experience implementing model monitoring, evaluation pipelines, and automated retraining systems. 

• Contributions to open-source AI projects, research publications, or patents.