Machine Learning Engineer (Defense)

XYZ Venture Capital; Boston (MA); On-site; USD 100,000 - 130,000; Full time

Job summary

Air Space Intelligence is seeking a talented engineer to join their core team in Boston. The role involves designing and deploying production-grade systems that integrate machine learning models into scalable pipelines. Candidates should have proficiency in Python and experience with ML frameworks like TensorFlow and PyTorch. A strong understanding of data structures and algorithms along with a collaborative mindset is essential. This position offers an opportunity to impact critical operations with modern technology.

Qualifications

• Proficiency in Python and experience with production ML tooling.
• Strong understanding of data structures, algorithms, and software engineering best practices.
• Familiarity with classical ML, deep learning, and MLOps concepts.

Responsibilities

• Design and deploy production-grade systems integrating machine learning models into scalable software pipelines.
• Develop and ship features leveraging ML to solve real-world optimization and prediction problems.
• Ensure robustness, maintainability, and performance at scale.

Skills

Proficiency in Python

Experience with production ML tooling

Strong understanding of data structures and algorithms

Familiarity with classical ML and deep learning

Experience in building reliable systems

Clear communication skills

Tools

TensorFlow

PyTorch

Kubernetes

AWS

Job description

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—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.