Applied AI Scientist

Vantor AI Engineer

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).

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, Tensor Flow, 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…