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About This Role
L3Harris is dedicated to recruiting and developing high\-performing talent who are passionate about what they do. Our employees are unified in a shared dedication to our customers’ mission and quest for professional growth. L3Harris provides an inclusive, engaging environment designed to empower employees and promote work\-life success. Fundamental to our culture is an unwavering focus on values, dedication to our communities, and commitment to excellence in everything we do.
L3Harris is the Trusted Disruptor in defense tech. With customers’ mission\-critical needs always in mind, our employees deliver end\-to\-end technology solutions connecting the space, air, land, sea and cyber domains in the interest of national security.
Job Title: Scientist, AI \& Cloud Security Architect
Job Code: 40743
Job Location: (On Site) Melbourne, FL; Salt Lake City, UT; Greenville, TX; Plano, TX; Rockwall, TX; Clifton, NJ; Rochester, NY
Job Schedule: Employees work 9 out of every 14 days – totaling 80 hours worked – and have every other Friday off
Job Description:
The Scientist is responsible for defining and driving the enterprise security strategy, target\-state architecture, and governance model for AI, cloud and container platforms across Azure and AWS. Reporting within the CISO organization, this role focuses on secure\-by\-design architecture, enterprise guardrails, risk\-informed adoption patterns, and security integration for modern platforms.
This leader partners closely with Security Architecture, Enterprise Architecture, Infrastructure and Data/AI teams to establish secure, scalable, and reusable platform patterns that enable rapid, compliant adoption of AI and cloud capabilities across the enterprise. The role serves as a strategic bridge between cybersecurity, enterprise technology, and engineering organizations to ensure AI, cloud, and container platform innovation is enabled with strong security, resilience, and governance foundations.
Essential Functions:
AI Security Strategy, Governance \& Architecture:
- Develop enterprise AI security and governance frameworks covering model lifecycle, data boundaries, and risk controls.
- Create AI reference architectures ensuring secure, compliant integration of generative and cloud\-native AI capabilities.
- Advise leadership on emerging AI technologies, associated risks, and secure platform adoption strategies.
Cloud Security Strategy \& Architecture:
- Define multi\-cloud security strategy, reference architectures, and enterprise guardrails for Azure, AWS, and hybrid environments.
- Standardize observability, networking, and compliance architectures across cloud platforms.
- Lead design reviews and maintain cloud architectural patterns enabling secure\-by\-design adoption.
- Partner with software engineering teams to embed security guardrails into their active implementations.
Container Platform Security
- Define security architecture, controls, and governance for Kubernetes, container orchestration, and runtime environments.
- Establish secure build, registry, and deployment patterns for containers across cloud and on‑prem platforms.
- Integrate container security posture, registry/image scanning, host OS scanning, and workload protection into enterprise standards.
Collaboration
- Work closely with stakeholders inside of IT and within other business functions to enforce the organizational security strategy.
- Translate complex technical issues into clear, actionable insights for stakeholders.
- Contribute to security standards, architectural guidance and policy improvements.
Qualifications:
- Bachelor’s Degree and minimum 12 years of prior relevant experience. Graduate Degree and a minimum of 10 years of prior related experience. In lieu of a degree, minimum of 16 years of prior related experience.
Preferred Skills:
- Familiarity with hybrid cloud connectivity patterns, network segmentation, and secure landing zones.
- Strong communication and executive‑level advisory capabilities.
- Thought leadership in AI transformation, cloud modernization, container platform initiatives.
- Experience contributing or leading security, architecture or governance boards.
- Knowledge of Zero Trust frameworks and enterprise‑grade security architectures.
- Ability to synthesize complex technical topics into actionable strategies for diverse stakeholders.
In compliance with pay transparency requirements, the salary range for this role in California, Massachusetts, New Jersey, Washington, and the Greater D.C, Denver, or NYC areas is $153,000\-$284,000\. The salary range for this role in Colorado state, Hawaii, Illinois, Maryland, Minnesota, New York state, and Vermont is $133,000\-$247,000\. This is not a guarantee of compensation or salary, as final offer amount may vary based on factors including but not limited to experience and geographic location. L3Harris also offers a variety of benefits, including health and disability insurance, 401(k) match, flexible spending accounts, EAP, education assistance, parental leave, paid time off, and company\-paid holidays. The specific programs and options available to an employee may vary depending on date of hire, schedule type, and the applicability of collective bargaining agreements.
\#LI\-NR1
L3Harris Technologies is proud to be an Equal Opportunity Employer. L3Harris is committed to treating all employees and applicants for employment with respect and dignity and maintaining a workplace that is free from unlawful discrimination. All applicants will be considered for employment without regard to race, color, religion, age, national origin, ancestry, ethnicity, gender (including pregnancy, childbirth, breastfeeding or other related medical conditions), gender identity, gender expression, sexual orientation, marital status, veteran status, disability, genetic information, citizenship status, characteristic or membership in any other group protected by federal, state or local laws. L3Harris maintains a drug\-free workplace and performs pre\-employment substance abuse testing and background checks, where permitted by law.
Please be aware many of our positions require the ability to obtain a security clearance. Security clearances may only be granted to U.S. citizens. In addition, applicants who accept a conditional offer of employment may be subject to government security investigation(s) and must meet eligibility requirements for access to classified information.
By submitting your resume for this position, you understand and agree that L3Harris Technologies may share your resume, as well as any other related personal information or documentation you provide, with its subsidiaries and affiliated companies for the purpose of considering you for other available positions.
L3Harris Technologies is an E\-Verify Employer. Please click here for the E\-Verify Poster in English or Spanish. For information regarding your Right To Work, please click here for English or Spanish.
Salary Context
This $133K-$247K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At L3Harris, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($190K) sits 13% below the category median. Disclosed range: $133K to $247K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
L3Harris AI Hiring
L3Harris has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Melbourne, FL, US, Palm Bay, FL, US. Compensation range: $247K - $247K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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