Senior Applied ML Scientist & Tech Lead - Spatial AI

Vantor (Maxar Intelligence); United States; Remote; USD 150,000 - 220,000; Full time

Benefits offered by this job

401(k) with company match

Mental health resources

Student loan repayment assistance

Adoption reimbursement

Pet insurance

Job summary

Vantor is seeking a senior ML engineer to design and deploy production-ready AI systems. You will collaborate with research, software, and product teams to translate cutting-edge models into real-world capabilities that inform decision making and automated workflows.

The role emphasizes end-to-end ML pipelines, large-scale data processing, and scalable inference on GCP, with ongoing model monitoring and retraining as needed.

Qualifications

• MS or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, or 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 one or more of the following areas.

Responsibilities

• Collaborate across research, engineering, and product teams to bring advanced AI capabilities into real-world applications.
• Design and deliver end-to-end ML pipelines, including data processing, training automation, evaluation, and scalable inference.
• Develop and deploy deep learning models in production environments.

Skills

ML systems in production

Cross-functional collaboration

End-to-end ML pipelines

Education

MS/PhD in CS/ML/AI/Applied Math or related field

Tools

GCP

Job description

Vantor is seeking a senior ML engineer to design and deploy production-ready AI systems. You will collaborate with research, software, and product teams to translate cutting-edge models into real-world capabilities that inform decision making and automated workflows.

The role emphasizes end-to-end ML pipelines, large-scale data processing, and scalable inference on GCP, with ongoing model monitoring and retraining as needed.