What are the responsibilities and job description for the Machine Learning Engineer position at Air Space Intelligence?
About Air Space Intelligence
ASI enables success for the world's most complex operations. From critical infrastructure to defense, we serve major airlines and U.S. and allied government organizations, providing our partners with a decision advantage from planning to operations. Backed by top-tier investors—including Andreessen Horowitz, Spark Capital, and Renegade Partners—we are boldly investing in R&D and growth to push the boundaries of what’s possible.
What you will do:
As part of our core engineering team, you will design and deploy production-grade systems that integrate machine learning models into scalable software pipelines. You’ll develop and ship features that leverage ML to solve real-world optimization and prediction problems, working with modern infrastructure like Kubernetes, AWS, and MLOps tooling. You’ll approach problems with a software engineer’s mindset—prioritizing robustness, maintainability, and performance at scale.
What we value:
We look at the interview process not as screening test but rather as an opportunity to simulate what it would look like working together. We build the interview process around you.
ASI enables success for the world's most complex operations. From critical infrastructure to defense, we serve major airlines and U.S. and allied government organizations, providing our partners with a decision advantage from planning to operations. Backed by top-tier investors—including Andreessen Horowitz, Spark Capital, and Renegade Partners—we are boldly investing in R&D and growth to push the boundaries of what’s possible.
What you will do:
As part of our core engineering team, you will design and deploy production-grade systems that integrate machine learning models into scalable software pipelines. You’ll develop and ship features that leverage ML to solve real-world optimization and prediction problems, working with modern infrastructure like Kubernetes, AWS, and MLOps tooling. You’ll approach problems with a software engineer’s mindset—prioritizing robustness, maintainability, and performance at scale.
What we value:
- Proficiency in Python and experience with production ML tooling and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Strong understanding of data structures, algorithms, and software engineering best practices.
- Familiarity with classical ML, deep learning, and MLOps concepts.
- Experience building and maintaining scalable, reliable systems that include ML components.
- A bias for simplicity and clarity in solving complex problems.
- Intellectual curiosity and willingness to collaborate.
- Clear communication and collaboration across cross-functional teams.
We look at the interview process not as screening test but rather as an opportunity to simulate what it would look like working together. We build the interview process around you.
