Machine Learning Engineer
Constellation Space · On-site
MeritLog read this listing from Constellation Space's Ashby job board and last checked it on September 9, 2026.
Source: the employer's Ashby job board. Open the original listing for current details.
Job details
- Work model
- On-site
- Salary
- $120K - $180K
- Location
- Seattle
- Occupation
- Computer Systems Engineers/Architects(O*NET 15-1299.08)
Hiring context
How this role compares at Constellation Space
Constellation Space has 4 live roles in MeritLog’s catalog across 2 job families, and 2 of them are in data & analytics. 4 of those listings publish a pay range, a disclosure rate of 100%.
Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.
What the role asks for
What you'd do
- 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.
What they're asking for
- B.S. or M.S. in Computer Science, Engineering, or equivalent experience.Education
- Proven experience deploying machine learning models into production.Skill
- Strong software engineering skills in Python and C++.Skill
- Experience with cloud platforms, containerization (Docker), and MLOps tools.Skill
Parsed by MeritLog from the employer’s own posting. The full description follows below.
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.
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