Sobre esta vaga de AI Software Engineer Level 3 or 4 (AHT) na Northrop Grumman
Description
At Northrop Grumman, our employees have incredible opportunities to work on revolutionary systems that impact people's lives around the world today, and for generations to come. Our pioneering and inventive spirit has enabled us to be at the forefront of many technological advancements in our nation's history - from the first flight across the Atlantic Ocean, to stealth bombers, to landing on the moon. We look for people who have bold new ideas, courage and a pioneering spirit to join forces to invent the future, and have fun along the way. Our culture thrives on intellectual curiosity, cognitive diversity and bringing your whole self to work — and we have an insatiable drive to do what others think is impossible. Our employees are not only part of history, they're making history.About our organization: As an engineer, you'll be joining our growing Aurora Software department which has 200+ Software Engineer, DevOps Engineer, Systems Administrator, Database Administrator, and Network Engineer peers from entry-level to the most senior chief engineers and architects. We have plenty of opportunities for career advancement into higher level technical roles or leadership positions. Our Software Department is part of a larger organization that includes Systems Engineering, Integration, and Test staff as well as a Hardware Engineering unit. This larger organization influences cross-program collaboration, professional development and training, as well as engagement and inclusion activities such as lunch-n-learns, campus events, and leadership mixers.
We support a small but mighty team that continues to innovate and set new benchmarks for our customers. If this sounds like an opportunity for you, Northrop Grumman Space Systems Sector would love to have you join our team as a Level 3 or 4 A.I. Software Engineer based out of Aurora, CO.
Basic Qualifications:
Level 3: Bachelor’s degree in science with 5+ years of software development experience; 3+ years with a Master's; or 4 additional years of experience in lieu of a degree.
Level 4: Bachelor’s degree in science with 8+ years of software development experience; 6+ years with a Master's; or 4 additional years of experience in lieu of a degree.
Must have an active Top Secret clearance with SCI eligibility at time of application.
Deep expertise in ML fundamentals – supervised, unsupervised, reinforcement learning, and statistical modeling.
Hands‑on experience with LLMs – model configuration, prompt engineering, output validation, and building agentic workflows.
Strong statistical background – hypothesis testing, experimental design, A/B testing, and data‑driven inference.
Proficient in Python – production‑grade code, testing, and refactoring.
Experience with the scientific Python stack – NumPy, pandas, PyTorch.
Version control & containerization – Git (branching, PR workflow) + Docker (or OCI‑compatible containers).
Linux‑based development – strong Bash/Unix scripting skills.
End‑to‑end problem‑solving – troubleshoot and optimize data pipelines, model training, and serving.
Self‑starter attitude – takes ownership and drives projects forward.
Preferred Qualifications:
Data engineering & cleaning – robust pipelines for noisy, imbalanced, or sparse real‑world datasets; experience with feature stores.
Database expertise – SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis) systems.
MLOps & productionization – CI/CD for ML (GitHub Actions, GitLab CI), experiment tracking (MLflow, Weights & Biases), model registry, automated testing, and monitoring/drift detection.
Model serving & APIs – FastAPI, Flask, gRPC, TorchServe, ONNX Runtime
Security & compliance awareness – data‑privacy regulations and secure model deployment practices.
Software engineering rigor – test‑driven development, code reviews, static analysis, design patterns, documentation.
Agile teamwork – Scrum/Kanban, cross‑functional collaboration, mentoring junior engineers.
MATLAB – for legacy code or signal‑processing tasks.
Fast‑paced, results‑driven environment – comfortable with shifting priorities and tight timelines.
Outstanding communication – clearly articulate technical concepts to both technical and non‑technical stakeholders; produce thorough documentation.
Knowledge of bias mitigation, fairness metrics, and responsible AI practices.
