Applied AI Scientist

maxar; United States; On-site; USD 128,000 - 187,000; Full time

Job summary

Vantor seeks a senior ML/AI engineer to design and deploy AI-driven geospatial applications. You will build end-to-end ML pipelines, productionize models including VLMs and multimodal systems, and collaborate with cross-functional teams to translate Earth observation challenges into scalable solutions.

You will work with domain experts to optimize performance, latency, and cost on cloud infrastructure, implement monitoring and retraining workflows, and stay current with foundation models and

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.
• Experience designing 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 VLMs, multimodal learning, reasoning models, LLMs, or computer vision/geospatial AI.
• Strong programming skills in Python with PyTorch, TensorFlow, or JAX.
• Experience deploying models into production using cloud infrastructure and containerization.
• Familiarity with distributed training and large-scale data processing.

Responsibilities

• Design, develop, and deploy AI-driven applications that transform geospatial data into actionable insights.
• Build end-to-end ML pipelines including data ingestion, preprocessing, training, evaluation, and production inference.
• Productionize reasoning models, VLMs, and multimodal AI systems combining imagery and structured data.
• Architect enterprise-grade training and experimentation frameworks with automated pipelines and reproducible evaluation.
• Create synthetic datasets and test harnesses to validate model performance in real-world environments.
• Collaborate with domain experts and engineers to translate Earth intelligence challenges into deployable AI solutions.
• Optimize models for scalability, latency, cost, and reliability on cloud infrastructure.
• Maintain production inference systems with monitoring, versioning, retraining workflows, and performance tracking.
• Stay current with advances in foundation models and generative AI, translating research into practical systems.
• Uphold high coding standards through reviews and disciplined experimentation.

Skills

ML pipelines

Python

Cloud deployments

Distributed training

Collaboration

Vision-language models

Multimodal learning

LLMs

Education

MS or PhD in Computer Science or related field

Tools

PyTorch

TensorFlow

JAX

Job description

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, 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 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:

To support pay transparency, Vantor includes salary ranges in all U.S. job postings. Starting pay for this role will fall within the listed range and will be based on factors such as experience, qualifications, skills, location, and market conditions. Candidates who meet the minimum requirements for the role should not expect to receive compensation at the top of the range. The listed range reflects the expected pay for this position, and final offers will be determined based on each candidate's experience, expertise, and alignment with the role.

• The base pay for this position within Colorado is: $128,000.00 - $170,000.00 - $187,000.00 annually.