Machine Learning Engineer

About the role

The Defense engineering team delivers systems that enable fast decision-making for aviation, defense, and energy missions. Production pipelines integrate machine learning models to compress analysis into seconds for critical infrastructure clients.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Production-grade systems are built and deployed to connect machine learning models with large-scale software workflows. These systems process restricted U.S. Government data and export-controlled workflows while remaining reliable at scale.

Features are developed and delivered to solve optimization and prediction challenges using machine learning. Kubernetes, AWS, and MLOps tooling carry real-world workloads in production environments.

Problem solving prioritizes robustness, maintainability, and performance at scale. Solutions emphasize simplicity and clarity to handle complex operational problems.

Reliable production ML systems are operated and maintained through strong data pipelines.

Production language models are worked with, covering prompt engineering, fine-tuning, retrieval-augmented generation, and frameworks such as LangChain. Cross-functional collaboration keeps communication clear when handling export-controlled technology. Intellectual curiosity fuels iterative improvement and knowledge sharing inside the team.

Requirements

Python proficiency is required, along with experience using production ML tooling and frameworks such as TensorFlow, PyTorch, and scikit-learn.

Production experience with large language models is required, including prompt engineering, fine-tuning, retrieval-augmented generation, and frameworks like LangChain.

A strong grasp of data structures, algorithms, and software engineering best practices is required.

Familiarity with classical machine learning, deep learning focused on transformer architectures, and MLOps concepts is required.

Experience building and maintaining scalable, reliable production ML systems with robust data pipelines is required, including expertise with Apache Beam, MLflow, and similar production-grade tools.

Commitment to high-quality ML engineering practices is required, including data versioning, experiment tracking, model governance, and automated testing pipelines.

Practical notes

Candidates must meet U.S. immigration status and location restrictions mandated on contracts. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Machine learning engineers apply software rigor to aviation, defense, and energy decision systems. Production ML relies on MLOps pipelines, data versioning, and experiment tracking to maintain reliability. Model governance and automated testing protect critical infrastructure workloads.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.