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.

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
  tovalidatemodel 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.

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

Pay Transparency: In support of pay transparency at Vantor, we disclose salary ranges on all U.S. job postings. The successful candidate's starting pay will fall within the salary range provided below and is determined based on job-related factors, including, but not limited to, the experience, qualifications, knowledge, skills, geographic work location, and market conditions. Candidates with the minimum necessary experience, qualifications, knowledge, and skillsets for the position should not expect to receive the upper end of the pay range.