
1001 - 5000 employees
đď¸ Defense
đŚ Logistics
đź Consulting
Defense ⢠Logistics ⢠Consulting
Vantor is a spatial intelligence company that builds an AI-ready "living globe" and a commercial imaging satellite constellation to unify sensor data across space, air, and ground. It provides software platforms and products for real-time tasking, sensor orchestration, high-resolution basemaps, radar and optical imagery, and autonomous ISR capabilities used by defense, intelligence, government, and commercial customers.
đĽ 0 minutes ago
đ California, Colorado, +1 more states â Remote
đľ $128k - $215.6k / year
â° Full Time
đĄ Mid-level
đ Senior
đ§ AI Research Scientist
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1001 - 5000 employees
đď¸ Defense
đŚ Logistics
đź Consulting
Defense ⢠Logistics ⢠Consulting
Vantor is a spatial intelligence company that builds an AI-ready "living globe" and a commercial imaging satellite constellation to unify sensor data across space, air, and ground. It provides software platforms and products for real-time tasking, sensor orchestration, high-resolution basemaps, radar and optical imagery, and autonomous ISR capabilities used by defense, intelligence, government, and commercial customers.
⢠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 covering data ingestion, preprocessing, feature engineering, training, evaluation, and production inference ⢠Productionize reasoning models, vision-language models, and multimodal AI systems combining imagery, geospatial signals, and structured data ⢠Architect enterprise-grade training and experimentation frameworks with automated pipelines, experiment tracking, benchmarking, and reproducible evaluation ⢠Create synthetic datasets and test harnesses to validate model performance, robustness, and edge-case behavior ⢠Translate Earth intelligence challenges into deployable AI solutions with domain experts, software engineers, product managers, and research partners ⢠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 foundation models, generative AI, multimodal learning, and reasoning systems, translating research advances into practical systems ⢠Maintain engineering standards through code reviews, documentation, experimentation discipline, and collaborative problem solving ⢠Help shape next-generation Earth AI capabilities through collaboration with research organizations and technology partners
⢠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 ⢠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 in vision-language models, multimodal learning, reasoning models, large language models, computer vision, or geospatial AI ⢠Strong programming skills in Python ⢠Experience with 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 ⢠U.S. Person status required: U.S. citizen, permanent resident, Asylee, or Refugee ⢠Certain roles may be subject to U.S. export control laws requiring U.S. Person status ⢠Preferred: experience 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 (GCP) ⢠Preferred: experience implementing model monitoring, evaluation pipelines, and automated retraining systems ⢠Preferred: contributions to open-source AI projects, research publications, or patents
⢠Competitive total rewards package ⢠Robust 401(k) with company match ⢠Mental health resources ⢠Student loan repayment assistance ⢠Adoption reimbursement ⢠Pet insurance ⢠Incentive eligible, with a target based on contribution, company performance, and/or individual results
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