Senior Geospatial AI Scientist - End-to-End ML & Production

MAXAR TECHNOLOGIES, INC.

United States

Remote

USD 128,000 - 196,000

Full time

14 days+

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Benefits offered by this job

Robust 401(k) with company match

Mental health resources

Student loan repayment assistance

Adoption reimbursement

Pet insurance

Job summary

Vantor is seeking an experienced AI/ML engineer to design and deploy systems that transform geospatial data into actionable insights. You will build end-to-end ML pipelines, productionize models, and collaborate with researchers and product teams to deliver deployable AI solutions.

The role requires deep learning expertise, strong Python coding, and hands-on experience with cloud deployments and distributed training.

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 Vision-language models, Multimodal learning, Reasoning models, LLMs, Computer vision or geospatial AI.
Strong programming skills in Python, with experience using frameworks such as PyTorch, TensorFlow, or JAX.
Experience deploying models into production environments in cloud and containerized setups.
Familiarity with distributed training and large-scale data processing.

Responsibilities

Design, develop, and deploy AI-powered applications that transform large-scale geospatial data into actionable insights.
Build and operate end-to-end ML pipelines including data ingestion, preprocessing, feature engineering, training, evaluation, and production inference.
Productionize reasoning models and multimodal AI systems combining imagery and data.
Create scalable experimentation and evaluation frameworks with reproducible workflows.
Collaborate with researchers, engineers, and product teams to translate Earth intelligence challenges into deployable AI solutions.
Monitor and optimize models for latency, cost, and reliability in cloud environments.
Stay current with advances in foundation models and generative AI to inform practical systems.