Principal Applied AI Scientist

Vantor is forging the new frontier of spatial intelligence, helping decision makers and operators navigate what's happening now and shape what's coming next. Vantor is a place for problem solvers, changemakers, and go-getters-where people are working together to help our customers see the world differently, and in doing so, be seen differently. Come be part of a mission, not just a job, where you can:
Shape your own future, build the next big thing, and change the world. To be eligible for this position, you must be a U.S. Person, defined as a U.S. citizen, permanent resident, Asylee, or Refugee. Export Control/ITAR:
Certain roles may be subject to U.S. export control laws, requiring U.S. person status as defined by 8 U.S.C. 1324b(a)(3). Please review the job details below.

Job Description Designs, develops and programs methods, processes, and systems to consolidate and analyze unstructured, diverse "big data" sources to generate actionable insights and solutions for client services and product enhancement. Interacts with product and service teams to identify questions and issues for data analysis and experiments. Develops and codes software programs, algorithms and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources.

Identifies meaningful insights from large data and metadata sources; interprets and communicates insights and findings from analysis and experiments to product, service, and business managers. Having wide-ranging experience, uses professional concepts and company objectives to resolve complex issues in creative and effective ways. Works on complex issues where analysis of situations or data requires an in-depth evaluation of variable factors.

Exercises judgment in selecting methods, techniques and evaluation criteria for obtaining results. Networks with key contacts outside own area of expertise. Determines methods and procedures on new assignments and may begin to coordinate activities of other team members.

Typically requires a minimum of 8 years of related experience with a Bachelor's degree; or 6 years and a Master's degree; or a PhD with 3 years experience; or equivalent experience.

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.

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…