Machine Learning Engineer

Air Space Intelligence • Boston, MA

Entry level Posted Feb. 27, 2026 Job link

Responsibilities

On the core engineering team at Air Space Intelligence, you'll design, deploy, and maintain production-grade systems that integrate machine learning models into scalable software pipelines. You will develop and ship ML-powered features to tackle real-world optimization and prediction problems using modern infrastructure such as Kubernetes, AWS, and MLOps tooling, while emphasizing robustness, maintainability, and performance at scale.

Commitments

Air Space Intelligence treats the interview process as a collaborative simulation of working together, tailoring the experience to each candidate rather than using it solely as a screening test.

Not Met Priorities

What still needs stronger evidence

Requirements

Proficiency in Python and experience with production ML tooling and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Experience using LLMs in production environments — covering prompt engineering, fine-tuning, RAG systems, and frameworks like LangChain
Strong understanding of data structures, algorithms, and software engineering best practices.
Familiarity with classical ML, deep learning with emphasis on transformer architectures, and MLOps concepts.
Experience building and maintaining scalable, reliable production ML systems with robust data pipelines, including expertise with Apache Beam, MLflow, and similar production-grade tools.
Commitment to high-quality ML engineering practices, including data versioning, experiment tracking, model governance, and automated testing pipelines.
A bias for simplicity and clarity in solving complex problems.
Intellectual curiosity and willingness to collaborate.
Clear communication and collaboration across cross-functional teams.

Preferred Skills

Familiarity with classical ML, deep learning with emphasis on transformer architectures, and MLOps concepts.
Experience building and maintaining scalable, reliable production ML systems with robust data pipelines, including expertise with Apache Beam, MLflow, and similar production-grade tools.
A bias for simplicity and clarity in solving complex problems.

About Air Space Intelligence
ASI's mission-critical technology powers decision-making across aviation, defense, energy, and other critical infrastructure domains. Backed by top-tier investors including Andreessen Horowitz, Spark Capital, and Renegade Partners, ASI delivers operational decision superiority—compressing days of analysis into seconds of action. ASI is leading the way and pushing 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).
Experience using LLMs in production environments — covering prompt engineering, fine-tuning, RAG systems, and frameworks like LangChain
Strong understanding of data structures, algorithms, and software engineering best practices.
Familiarity with classical ML, deep learning with emphasis on transformer architectures, and MLOps concepts.
Experience building and maintaining scalable, reliable production ML systems with robust data pipelines, including expertise with Apache Beam, MLflow, and similar production-grade tools.
Commitment to high-quality ML engineering practices, including data versioning, experiment tracking, model governance, and automated testing pipelines.
A bias for simplicity and clarity in solving complex problems.
Intellectual curiosity and willingness to collaborate.
Clear communication and collaboration across cross-functional teams.
How Do We Hire
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.