Machine Learning Engineer (Defense)
At a glance
Mid-level Machine Learning Engineering role at Air Space Intelligence. Boston · full-time · $135,000–$265,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build production software pipelines that bring machine learning models into scalable systems
- Ship ML-powered features for prediction and optimization problems
- Use infrastructure such as Kubernetes and AWS to deliver ML systems
- Keep systems reliable, maintainable and performant as they scale
What you bring
- You’re proficient in Python and have used ML frameworks such as TensorFlow, PyTorch or scikit-learn
- You’ve deployed LLMs and worked on prompts, fine-tuning, retrieval-augmented generation and related frameworks
- You understand software engineering fundamentals, classical ML, transformer-based deep learning and MLOps
- You’ve built dependable production ML systems and data pipelines, using tools such as Apache Beam or MLflow
Who this fits
This role suits a mid-level engineer who favors clear, robust solutions and can collaborate and communicate across teams. Work involves export-controlled technology and restricted U.S. government data; some duties require U.S. immigration status and location restrictions, and offers depend on obtaining the necessary authorizations.
From the employer
About ASI
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 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.