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About This Role
Company Information:
Elbit America is a leading provider of high\-performance products, system solutions, and support services focusing on the defense, homeland security, commercial aviation, and medical instrumentation markets. With facilities throughout the United States, Elbit Systems of America is dedicated to supporting those who contribute daily to the safety and security of the United States. Elbit Systems of America, LLC is wholly owned by Elbit Systems Ltd. (NASDAQ: ESLT and TASE: ESLT), a global high\-technology company engaged in a wide range of programs for innovative defense and commercial applications. For additional information, visit: ElbitAmerica.com or follow us on YouTube.
Job Summary:
The Principal AI Engineer plays a pivotal role in driving innovation and developing advanced artificial intelligence solutions for defense applications. This position involves leading the design, implementation, and optimization of AI systems for autonomous platforms, mission planning, and predictive analytics. The role requires collaboration with multidisciplinary teams to integrate AI solutions with cutting\-edge hardware and ensure compliance with safety, ethical, and regulatory standards. As a technical leader, this role mentors team members, manages complex datasets, and stays ahead of emerging AI technologies to maintain a competitive edge. With a focus on security and performance, this role is essential for advancing defense technologies and supporting critical mission objectives.
Responsibilities and Tasks:
- Lead the design and development of advanced AI algorithms for defense applications, including autonomous systems, decision\-making, and predictive analytics.
- Oversee the integration of AI solutions with hardware platforms, such as drones, aircraft, or missile systems, ensuring seamless operation.
- Manage large datasets, including collection, preprocessing, and analysis, to train and validate AI models.
- Optimize AI models for performance, reliability, and scalability in real\-world defense scenarios.
- Conduct rigorous testing, including hardware\-in\-the\-loop (HIL) and software\-in\-the\-loop (SIL) simulations, to validate AI systems under various operational conditions.
- Work closely with multidisciplinary teams, including software engineers, systems engineers, and domain experts, to align AI solutions with mission objectives.
- Ensure AI systems meet stringent safety, ethical, and regulatory standards specific to defense applications.
- Stay updated on the latest advancements in AI and Machine Learning to drive innovation and maintain a competitive edge.
- Provide technical leadership and mentorship to junior engineers and team members.
- Prepare detailed documentation and reports for stakeholders, including government agencies and contractors.
Knowledge (Education/License/Certification, Prior Experience)
- Bachelor’s or master’s degree in computer science, Engineering, Mathematics, or related field (PhD preferred)
- 6\+ years of professional experience in algorithm development, machine learning, or AI engineering
- Proficiency in machine learning, deep learning, reinforcement learning, and natural language processing techniques.
- Advanced programming skills in languages such as Python, C\+\+, Java, or R, along with experience in AI frameworks (TensorFlow, PyTorch, etc.)
- Strong foundation in linear algebra, statistics, probability, optimizations, and algorithms.
- Expertise in managing and processing large datasets, including structured and unstructured data.
- Experience in optimizing AI systems for performance, scalability, and reliability in defense environments.
- Proficient in designing rigorous validation and testing methodologies for AI systems.
- Understanding of defense\-related applications, including autonomous systems, situational awareness, and mission planning.
- Familiarity with cybersecurity principles and practices to safeguard AI systems.
Skills and Abilities:
- Skills in developing, training, testing, and deploying AI models in real\-world scenarios.
- Ability to integrate AI systems with hardware platforms and ensure seamless functionality.
- Strong ability to analyze complex problems and develop innovative AI\-driven solutions.
- Ability to lead teams, mentor junior engineers, and provide technical direction.
- Excellent collaboration skills to work with multidisciplinary teams including engineers and domain experts.
- Strong written and verbal communication skills for presenting findings, preparing documentation, and liaising with stakeholders.
- Willingness to stay updated with emerging AI technologies and trends.
- Ability to propose novel solutions and push the boundaries of existing technology.
Travel/Physical Requirements:
- Willingness to travel CONUS.
- \*This role is fully remote for candidates within the United States\*
\#LI\-TS1
\#Remote
Here Are Some of the Great Benefits We Offer:
- Most locations offer a 9/80 schedule, providing every other Friday off
- Competitive compensation \& 401(k) program to plan for your future
- Robust medical, dental, vision, \& disability coverage with qualified wellness discounts
- Basic Life Insurance and Additional Life \& AD\&D Insurances are available
- Flexible Vacation \& PTO
- Paid Parental Leave
- Generous Employee Referral Program
- Voluntary Benefits Available: Longer Term Care, Legal, Identity Theft, Pet Insurance, and more
- Voluntary Tricare Supplement available for military retirees
This job description does not list all the duties of the job. You may be asked by your supervisors or managers to perform other duties. The employer has the right to revise this job description at any time. The job description is not an employment contract. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of this position. Elbit America is an equal opportunity employer as to all protected groups, including protected veterans and individuals with disabilities.
\*\*\* If you encounter issues with your application, please email technicalsupport@elbitsystems\-us.com\*\*\*
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Elbit Systems of America, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Elbit Systems of America AI Hiring
Elbit Systems of America has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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