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About This Role
RELOCATION ASSISTANCE: Relocation assistance may be available
CLEARANCE REQUIRED FOR START: No
CLEARANCE TYPE: Secret
TRAVEL: Yes, 10% of the TimeDescription
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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.This requisition may be filled at the Level 2 or Level 3 based on requirements listed below.
The Northrop Grumman Aeronautics Sector in Space Park Redondo Beach/El Segundo, CA is seeking a highly motivated AI Software Engineer to join our team. Our AI team is growing and there is an opportunity to work closely with senior AI subject matter experts while simultaneously supporting the business. We are looking for a talented AI Software Engineer who can take small to medium sized projects from concept to production quickly with limited technical guidance. You will build and fine‑tune Retrieval‑Augmented Generation (RAG) pipelines, Agentic AI pipelines, and large language model (LLM) solutions that solve concrete business problems. You will work closely with product owners, data scientists, and senior engineers to ship reliable, scalable AI services. Successful candidates will have demonstrated the ability to build AI solutions to solve customer problems. They will have a track record as an effective communicator and problem solver who is able to develop and maintain good working relationships with internal and external stakeholders. The selected candidates will work closely with AI engineers and business partners to accomplish the following:
- Work with customers to define, develop, and deliver AI solutions to meet mission and sector needs.
- Design, develop, document, test and debug applications software and systems that contain logical and mathematical solutions.
- Collaborate with cross\-functional teams to deploy machine learning algorithms for testing and is in production systems.
- Maintain understanding of the cutting\-edge technologies in AI/ML.
- Regularly demonstrate progress to customers.
- Rapid Prototyping \& Delivery – Turn medium‑priority use‑case specifications into working AI prototypes within 2‑4 weeks and iterate to production quality.
- RAG, Agentic AI, and LLM Development – Design and implement retrieval‑augmented generation pipelines and agentic workflows integrating pretrained LLMs (e.g., GPT‑4/5/OSS, Llama) though APIs and self\-hosted model deployments.
- Model Integration – Embed fine‑tuned models into existing micro‑service architectures (REST/GraphQL, gRPC) and expose them via APIs or internal SDKs.
- Performance \& Reliability – Set up monitoring, logging, and automated testing (unit, integration, latency) for AI services; troubleshoot model drift and latency issues.
- Data Preparation – Collaborate with data engineers to curate, clean, and annotate domain‑specific datasets for fine‑tuning and evaluation.
- Documentation \& Knowledge Sharing – Write clear technical documentation, create example notebooks, and mentor junior teammates on AI best practices.
- Continuous Improvement – Stay current with the latest LLM research, open‑source tools, and industry trends; propose upgrades to the AI stack.
Basic Qualifications:
- For level 2 consideration: Bachelor's degree in Computer Science or related STEM field and 2 years of relevant experience or Master's degree in Computer Science or related STEM field with relevant entry level experience
- For Level 3 consideration: Bachelor's degree in Computer Science or related STEM degree and 5 years with Master's degree in Computer Science or related STEM field and 3 years of relevant experience
- Entry level professional AI/software engineering experience; demonstrated ability to ship production\-grade code
- Ability to obtain and maintain final US Government Secret Clearance
- Must have ability to obtain and maintain Program Access (PAR) within a reasonable period of time, as determined by the company to meet its business needs
- Strong problem‑solving skills and ability to work independently on moderately scoped projects.
- Demonstrated ability to carry out rapid prototyping and proof\-of\-concept demonstrations using novel AI methods to solve real\-world challenges.
- Ability to effectively communicate system properties and results to technical staff as well as non\-technical management and leadership stakeholders.
- Proficiency in Python (including libraries such as PyTorch, Hugging Face Transformers, LangChain, LangGraph).
- Familiarity with RAG and agentic AI concepts: vector stores (e.g., Pinecone, FAISS, Milvus), embeddings, similarity search, prompt engineering, function calling or tool use, structured outputs
- Experience developing AI solutions using LLMs, Agentic AI, MLOps pipelines, Cloud services, ETL, evaluation of AI solution, services, containers and container orchestration
- Hands‑on experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
- Understanding of software engineering best practices: version control (Git), CI/CD pipelines, automated testing, code reviews.
Preferred Qualifications:
- Active and final US Government Secret Clearance
- Ability to obtain and maintain a U.S. Government Top Secret security clearance (U.S. citizenship is a pre\-requisite)
- Experience working with DoD/DoW customer
- Experience building and training large neural networks on custom datasets
- Strong understanding of neural network fundamentals, including model architectures, loss functions, optimization, regularization, and evaluation methodologies
- Experience with supervised learning, unsupervised learning, reinforcement learning, and statistical modeling
- Experience with deep learning frameworks (PyTorch, TensorFlow, JAX)
- Experience working in Agile software development environment
- Experience with leading teams and actively participating in the development of winning white papers and proposals for DoD customers
- Familiarity with explainable AI, adversarial AI, responsible AI (NIST AI Risk Framework)
- Familiarity with signal processing, information theory, topological data analysis, and/or genetic algorithms.
Primary Level Salary Range: $111,000\.00 \- $166,600\.00
Secondary Level Salary Range: $137,800\.00 \- $206,800\.00
The above salary range represents a general guideline; however, Northrop Grumman considers a number of factors when determining base salary offers such as the scope and responsibilities of the position and the candidate's experience, education, skills and current market conditions.
Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay. Annual bonuses are designed to reward individual contributions as well as allow employees to share in company results. Employees in Vice President or Director positions may be eligible for Long Term Incentives. In addition, Northrop Grumman provides a variety of benefits including health insurance coverage, life and disability insurance, savings plan, Company paid holidays and paid time off (PTO) for vacation and/or personal business.
The application period for the job is estimated to be 20 days from the job posting date. However, this timeline may be shortened or extended depending on business needs and the availability of qualified candidates.
Northrop Grumman is an Equal Opportunity Employer, making decisions without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other protected class. For our complete EEO and pay transparency statement, please visit http://www.northropgrumman.com/EEO. U.S. Citizenship is required for all positions with a government clearance and certain other restricted positions.
Salary Context
This $111K-$206K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Northrop Grumman, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($158K) sits 14% below the category median. Disclosed range: $111K to $206K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Northrop Grumman AI Hiring
Northrop Grumman has 6 open AI roles right now. They're hiring across Data Scientist, AI Software Engineer, AI/ML Engineer. Positions span Melbourne, FL, US, Dulles, VA, US, Redondo Beach, CA, US. Compensation range: $187K - $258K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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