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. 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 da