Applied AI Scientist

About this role

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.

● The base pay for this position within New Jersey is: $128,000.00 - $170,000.00 - $187,000.00 annually.

● The base pay for this position within Delaware is: $128,000.00 - $170,000.00 - $187,000.00 annually.   ● The base pay for this position within the Washington, DC metropolitan area is: $140,000.00 - $187,000.00 - $205,700.00 annually.

● The base pay for this position within California is: $147,000.00 - $196,000.00 - $215,600.00 annually.

For all other states, we use geographic cost of labor as an input to develop market-driven ranges for our roles, and as such, each location where we hire may have a different range.

Benefits: Vantor offers a competitive total rewards package that goes beyond the standard, including a robust 401(k) with company match, mental health resources, and unique perks like student loan repayment assistance, adoption r

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