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Airspace Intelligence.Com

Machine Learning Engineer (Defense)

Boston, USUnited States
FullTime · 1. Defense
POSTED 3 APR 2025VERIFIED 7H AGO
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Role
584 days opentrue age
Posted 3 Apr 2025
Payats field
$170k–300kposted
▲ +21% vs market median $194k
$168k$300k
Market p25–p75
$168k–$226k
Boston · n=28 · 365d to 6 Nov
CompanyAirspace Intelligence.Com →
34open roles
Opened · 30d
+0
Closed · 30d
−0
6d of snapshots · 30d window
Market
3,285open · ML / AI
Opened · 7d
+1846% of open
All ml / ai
6,677open
All tracked boards · live 9 Nov · n=3,285
PythonAWSKubernetesSparkPyTorchTensorFlowLLMs

About ASI

ASI is the frontier AI company that dynamically orchestrates critical infrastructure and defense assets, across air traffic control, energy, logistics, and combat operations. ASI's systems simulate physical operations, hours, days, and weeks ahead, so the people running air traffic control, the electric grid, and military operations can catch problems early and act before they spread. ASI is powering vital infrastructure for customers like United Airlines, National Grid, the Federal Aviation Administration, Delta Air Lines and the Department of War. ASI is headquartered in Boston with offices in Washington, DC, Denver, and Europe, and is backed by leading investors, including Andreessen Horowitz, Citadel Securities, and Spark Capital.

What You Will Do

As part of our Defense 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 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.

ASI works with export controlled technology and restricted U.S. Government data, including on contracts mandating U.S. immigration status and location restrictions for performing personnel. Employment offers are contingent on ability to timely obtain all required authorizations for contemplated job duties.

Source · Airspace Intelligence.ComFirst seen 4 Nov1 editsOriginal ↗