Interested in this AI/ML Engineer role at Northrop Grumman?
Apply Now →Skills & Technologies
About This Role
RELOCATION ASSISTANCE: No relocation assistance available CLEARANCE REQUIRED FOR START: No CLEARANCE TYPE: None TRAVEL: Yes, 10% of the TimeDescription
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. Expand your horizons, advance your career, and contribute to a secure future for generations to come. Northrop Grumman’s Space Sector invites you to bring your pioneering spirit to our collaborative teams. As an AI / Automation Engineer – Level 3 or 4 in Chandler, AZ, you’ll be a key contributor to missions of national importance, helping define what’s possible from day one. This position is 100% onsite and cannot accommodate telecommute work.
We are seeking a highly motivated AI / Automation Engineer to join our dynamic team. In this role, you will design, develop, and maintain automation solutions that impact engineering and infrastructure across the organization. You will combine hands-on software engineering with cutting-edge AI and ML to automate engineering, DevOps, infrastructure tasks, processes, and workflows.
This role sits at the intersection of AI, automation, CI/CD, and infrastructure, giving you the opportunity to:
- Build and integrate AI-powered capabilities into engineering workflows
- Design and operate robust CI/CD DevOps pipelines
- Support containerized, cloud and on-premise environments at scale
Join us and be part of a team driving innovative solutions at the forefront of space systems and advanced software.
Your Role and Impact
In this role, you will:
- Design, develop, document, test, and debug AI and ML capabilities to automate engineering and infrastructure workflows.
- Automate processes by building and maintaining CI/CD pipelines and AI-enabled tooling within a DevOps environment.
- Build and maintain agent orchestration frameworks that integrate with LLM services, system services, and engineering tools.
- Develop, deploy, and maintain containerized applications (e.g., Docker, Kubernetes) to ensure portability, scalability, and reliability.
- Implement and support RAG pipelines and vector database integrations to enable intelligent retrieval and decision-making.
- Provide infrastructure support by troubleshooting issues, optimizing performance, and maintaining high availability and observability of systems.
- Collaborate closely with engineering, DevOps, and infrastructure teams to identify automation opportunities and bring them to production.
- Communicate clearly with technical and non-technical stakeholders, both in writing and verbally.
- Perform other related duties as assigned.
If this job description reads like it was written specifically for you, we’d like to talk with you.
Basic Qualifications for Level 3:
- Bachelor’s degree with 5 years of professional experience – OR – Master’s degree with 3 years of professional experience – OR – PhD with 1 year of professional experience.
- Must have the ability to obtain and maintain a U.S. Government DoD Secret security clearance.
- Hands-on experience working in Linux environments, including scripting and system configuration.
- Proficiency in Python or Go (Golang) with a strong background in software development, debugging, and testing.
- Experience with CI/CD, version control systems, and automated pipelines for application deployment.
- Experience developing and deploying AI and/or Machine Learning (ML) capabilities.
- Experience with APIs and integrating models into applications or agentic frameworks ( e.g. Mastra, LangChain, or similar toolkits) for building autonomous agents.
- Experience developing and hosting AI/ML models in cloud-based environments (e.g., AWS, Azure) and/or on-premise containerized infrastructure.
- Experience with Retrieval-Augmented Generation (RAG) pipelines and vector databases.
- Strong understanding of Large Language Models (LLMs), including concepts such as fine-tuning, hosting, and inference optimization.
- Demonstrated ability to independently own and deliver complex automation and/or AI solutions from concept through deployment.
Basic Qualifications for Level 4:
- Bachelor’s degree with 8 years of professional experience – OR – Master’s degree with 6 years of professional experience – OR – PhD with 4 years of professional experience.
- Must have the ability to obtain and maintain a U.S. Government DoD Secret security clearance.
- Hands-on experience working in Linux environments, including scripting and system configuration.
- Proficiency in Python or Go (Golang) with a strong background in software development, debugging, and testing.
- Experience with CI/CD, version control systems, and automated pipelines for application deployment.
- Experience developing and deploying AI and/or Machine Learning (ML) capabilities.
- Experience with APIs and integrating models into applications or agentic frameworks ( e.g. Mastra, LangChain, or similar toolkits) for building autonomous agents.
- Experience developing and hosting AI/ML models in cloud-based environments (e.g., AWS, Azure) and/or on-premise containerized infrastructure.
- Experience with Retrieval-Augmented Generation (RAG) pipelines and vector databases.
- Strong understanding of Large Language Models (LLMs), including concepts such as fine-tuning, hosting, and inference optimization.
- Demonstrated ability to independently own and deliver complex automation and/or AI solutions from concept through deployment.
Preferred Qualifications:
- Active DoD Secret Security Clearance or higher
Curious about all the exciting developments with the Northrop Grumman Space Sector? Click the link below:
https://www.northropgrumman.com/space
Working at Northrop Grumman is more than just a paycheck. We offer a comprehensive Total Rewards and benefits package designed to help you thrive at work and in life. For more information on our Total Rewards package, please visit our Total Rewards site.
Northrop Grumman Total Rewards
#AZSpace
#SpaceEngJobs
Primary Level Salary Range: $98,400.00 - $147,600.00 Secondary Level Salary Range: $122,800.00 - $184,200.00 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 $98K-$184K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($141K) sits 33% below the category median. Disclosed range: $98K to $184K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Northrop Grumman AI Hiring
Northrop Grumman has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Redondo Beach, CA, US, Chandler, AZ, US, Melbourne, FL, US. Compensation range: $147K - $258K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.