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RELOCATION ASSISTANCE: No relocation assistance available CLEARANCE REQUIRED FOR START: No CLEARANCE TYPE: Secret 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.Description
At Northrop Grumman, we 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.
Northrop Grumman is seeking a Staff AI Software Engineer to support the development, integration, and deployment of advanced AI-enabled software solutions to support our Systems Engineering Integration & Test (SEIT) department. As a member of the SEIT department you’ll work in a fast-paced team environment alongside physicists, design engineers, and superconducting foundry engineers to make our technologies a reality. This position will serve on-site at Spectrum Center, San Diego*.*
Responsibilities:
- Lead the design, development, integration, and sustainment of advanced AI-enabled software products and supporting systems across cloud and local/on-premises environments.
- Initiate advanced development efforts and help shape technical direction from concept through deployment, transition, and sustainment.
- Evaluate AI/ML use cases based on mission need, data availability, model suitability, hardware constraints, latency, scalability, security, and operational risk.
- Guide teams through implementation of complex AI and software solutions across software, systems, cyber, networking, DevOps, and operations domains.
- Translate customer, mission, and business objectives into secure, scalable, and maintainable technical solutions that account for cost, schedule, and performance constraints.
- Lead technical trade studies, architecture assessments, and solution analyses to support complex engineering and product decisions.
- Develop and refine AI systems using appropriate model adaptation, fine-tuning, evaluation, and deployment approaches for operational use.
- Design AI/software solutions that effectively leverage available compute resources, including GPU, memory, storage, networking, and deployment limitations.
- Secure and harden end-to-end systems, including infrastructure, platforms, applications, data flows, models, and development pipelines.
- Evaluate and securely leverage open-source frameworks, models, and tools while maintaining sound engineering, security, and supportability practices.
- Identify high-value technical improvement opportunities and independently drive solutions that improve performance, resiliency, efficiency, security, or cost.
- Present technical strategies, risks, and recommendations to program leadership, directors, customers, and other stakeholders.
- Support proposal activities, technical planning, budgeting, scheduling, and execution planning for advanced development efforts.
- Mentor engineers, strengthen technical networks across teams, and help elevate engineering standards and execution across the organization.
Basic Qualifications for Principal AI Software Engineer:
- Bachelor’s degree in Computer Science, Software Engineering, Systems Engineering, or related STEM discipline with 12 years of relevant experience; or 10 years with a Master’s degree; or 8 years with a PhD.
- Candidate must be a US Citizen.
- Proven ability to move AI capabilities from research or prototype stage into reliable operational use.
- Integration of AI into larger end-to-end systems.
- Experience evaluating AI/ML solutions against data quality, operational constraints, compute availability, and mission suitability.
- Experience deploying software or AI systems into complex production, lab, or mission environments.
- Experience performing technical trade studies and making architecture decisions across performance, cost, security, and maintainability.
- Experience integrating AI capabilities into larger end-to-end systems rather than isolated prototypes.
- Experience with modern development practices including version control, test automation, CI/CD, and containerized deployment workflows.
- Clearance: A current/active DoD Secret clearance.
Preferred Qualifications for Principal AI Software Engineer:
- Experience leading advanced AI/ML development efforts from early concept through deployment and sustainment.
- Experience evaluating and deploying AI solutions with consideration for accuracy, latency, throughput, robustness, and hardware efficiency.
- Experience with computer vision, robotics, autonomous systems, or swarm/distributed intelligent systems.
- Experience with GPU optimization, distributed inference, or deployment in constrained compute environments.
- Experience fine-tuning large language models or related foundation models using methods such as LoRA, QLoRA, domain-adaptive pretraining, or similar adaptation approaches.
- Working knowledge across adjacent domains such as cyber security, networking, DevOps, systems engineering, or platform engineering.
- Experience securing end-to-end AI/software platforms spanning infrastructure, applications, data, models, interfaces, and deployment pipelines.
- Experience evaluating and incorporating open-source models, frameworks, and tooling in a secure and supportable manner.
- Familiarity with modern software delivery practices including CI/CD, containerization, automation, and platform operations.
- Demonstrated ability to influence technical strategy, advise management or customers on advanced technical efforts, and represent the organization on complex initiatives.
- Experience supporting proposals, technical planning, estimation, budget discussions, and schedule-driven execution.
- Proven ability to mentor others, build technical networks, and drive collaboration across large interdisciplinary teams.
- Track record of delivering measurable mission or business value through new capability development, cost savings, force multiplication, or operational efficiency.
Primary Level Salary Range: $214,200.00 - $321,200.00 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 $214K-$321K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($267K) sits 26% above the category median. Disclosed range: $214K to $321K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Northrop Grumman AI Hiring
Northrop Grumman has 13 open AI roles right now. They're hiring across Research Scientist, AI Software Engineer, AI/ML Engineer, Data Scientist. Positions span WV, US, Chandler, AZ, US, Remote, US. Compensation range: $123K - $321K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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