Applied AI Scientist

Vantor

Applied AI Scientist building production AI systems for Vantor’s spatial intelligence platform. Transforming geospatial data, imagery, and multimodal models into actionable Earth intelligence.

Posted 8/17/2026full-timeRemote • California, Colorado, New Jersey • 🇺🇸 United StatesMid-LevelSenior💰 $128,000 - $215,600 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and deploying AI-driven applications, particularly in the context of geospatial data and machine learning systems. Proficient in building end-to-end ML pipelines and optimizing models for production environments using modern cloud infrastructure.

Highest-signal resume keywords

Machine Learning Systems DeploymentEnd-to-End ML Pipeline DesignDeep Learning Model DevelopmentPython ProgrammingCloud Infrastructure Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningData ProcessingModel OptimizationExperiment TrackingFeature EngineeringModel EvaluationGeospatial AIComputer VisionMultimodal Learning

Soft Skills

CollaborationProblem SolvingCommunication

Tools & Technologies

PyTorchTensorFlowJAXGoogle Cloud PlatformContainerized Systems

Certifications & Qualifications

MS or PhD in Computer ScienceMachine LearningArtificial Intelligence

Industry Keywords

Geospatial DataRemote SensingSatellite ImageryEarth Observation SystemsFoundation Models

Tech Stack

Tools & technologies

CloudGoogle Cloud PlatformPythonPyTorchRemote SensingTensorflow

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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

Benefits

Comp & perks

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