Machine Learning Engineer

Constellation Space; Seattle (WA); On-site; USD 95,000 - 135,000; Full time

Job summary

Constellation Space in Seattle is searching for a Machine Learning Engineer to connect AI research with flight systems. You will optimize and deploy machine learning models that enhance Constellation’s operations in space.

Your responsibilities include working on MLOps pipelines and ensuring models perform effectively under production conditions, optimizing for spacecraft hardware. Ideal candidates have strong software engineering skills and a solid educational background in Computer Science or Engineering.

Qualifications

• Experience with deploying machine learning models into production.
• Strong software engineering background with Python and C++.
• Familiarity with MLOps tools and methodologies.

Responsibilities

• Deploy, monitor, and maintain ML models in production environments.
• Build robust MLOps pipelines for model training and integration.
• Optimize algorithms for low-latency inference on spacecraft hardware.
• Collaborate with data scientists and engineers to integrate AI capabilities.

Skills

Machine Learning

Python

C++

Cloud Platforms

Containerization (Docker)

MLOps

Education

B.S. or M.S. in Computer Science, Engineering, or equivalent experience

Job description

The Role

We are looking for a Machine Learning Engineer to bridge the gap between AI research and production-grade flight systems. You will optimize, deploy, and scale machine learning models that directly impact Constellation’s orbital systems and ground operations.

Responsibilities

• Deploy, monitor, and maintain ML models in production environments.
• Build robust MLOps pipelines for continuous training and integration of models using telemetry data.
• Optimize algorithms for low-latency inference on edge devices (spacecraft hardware).
• Collaborate with data scientists and flight software engineers to integrate AI capabilities into core flight systems.

Requirements

• B.S. or M.S. in Computer Science, Engineering, or equivalent experience.
• Proven experience deploying machine learning models into production.
• Strong software engineering skills in Python and C++.
• Experience with cloud platforms, containerization (Docker), and MLOps tools.