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, 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
  .

• Contributi…