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Vantor
Vantor
1,001 – 5,000 Employees
ConsultingDefenseLogistics
Vantor is a spatial intelligence company developing an AI-ready living globe and a commercial imaging satellite constellation. Its platforms bring together sensor data from space, air, and ground to support real-time tasking, sensor orchestration, high-resolution basemaps, radar and optical imagery, and autonomous intelligence, surveillance, and reconnaissance capabilities. Vantor serves defense, intelligence, government, and commercial customers, with teams working across technologies relevant to satellite imaging, geospatial data, logistics, and consulting.
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Principal Applied AI Scientist - Vantor, Remote United States

Lead the development of production AI and machine learning systems for Vantor’s spatial intelligence platform. Apply geospatial data, imagery, and multimodal models to generate operational intelligence.

USUnited States
$150,000 – $220,000 / year
RemoteFull-timeLeadEnglish RequiredAI Research ScientistMachine Learning Engineer
CloudGoogle Cloud PlatformPythonPyTorchRemote SensingTensorflow

Description

  • Develop and deploy AI applications that turn large-scale geospatial data into actionable and predictive intelligence.
  • Build and manage complete machine learning pipelines spanning data ingestion, preprocessing, feature engineering, training, evaluation, and production inference.
  • Bring reasoning, vision-language, and multimodal models into production using imagery, geospatial signals, and structured data.
  • Design enterprise training and experimentation frameworks with automated workflows, experiment tracking, benchmarking, and reproducible evaluation.
  • Develop synthetic datasets and testing harnesses to assess model performance, robustness, and edge-case behavior.
  • Work with domain specialists, software engineers, product managers, research collaborators, and technology partners.
  • Improve model and inference performance for scale, latency, cost efficiency, and reliability in modern cloud environments.
  • Maintain production inference systems, including monitoring, model versioning, retraining workflows, and performance measurement.
  • Evaluate developments in foundation models, generative AI, multimodal learning, and reasoning systems for practical application.
  • Uphold engineering quality through code reviews, documentation, disciplined experimentation, and collaborative problem solving.
  • Contribute to the development of next-generation Earth AI capabilities.

Requirements

  • Must hold U.S. Person status as a U.S. citizen, permanent resident, Asylee, or Refugee.
  • Master’s or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, or a related technical discipline, or equivalent practical experience.
  • At least five years of experience building and deploying machine learning systems in production.
  • Experience designing and delivering end-to-end machine learning pipelines.
  • Experience developing and deploying deep learning models.
  • Experience with vision-language models, multimodal learning, reasoning models, large language models, computer vision, or geospatial AI.
  • Advanced Python programming skills.
  • Experience with PyTorch, TensorFlow, or JAX.
  • Experience creating reproducible experimentation pipelines.
  • Experience deploying models with modern cloud infrastructure and containerized systems.
  • Familiarity with distributed training, large-scale data processing, and model optimization methods.
  • Ability to work effectively across research, engineering, and product teams.
  • Typically requires eight years of related experience with a bachelor’s degree, six years with a master’s degree, or three years with a PhD, or equivalent experience.
  • Preferred experience working with geospatial data, remote sensing, satellite imagery, or Earth observation systems.
  • Preferred experience building or fine-tuning foundation models, multimodal models, or agentic AI systems.
  • Preferred familiarity with Google Cloud Platform.
  • Preferred experience implementing model monitoring, evaluation pipelines, and automated retraining systems.
  • Preferred contributions to open-source AI projects, research publications, or patents.

Benefits

  • 401(k) plan with company matching contributions.
  • Mental health support resources.
  • Student loan repayment assistance.
  • Adoption reimbursement.
  • Pet insurance.
  • Eligibility for incentive compensation based on contribution, company performance, and individual results.

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