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About This Role
Job Description
We’re hiring a senior systems-level designer who brings AI Harness Engineering and Development best practices to complex, secure enterprise systems. You’ll shape end-to-end architectures with frameworks integrating multiple AI and Data Enclaves, Systems and Interfaces. Designing for measurable outcomes, integration with AI & Zero Trust security, anti-hallucination and token optimization frameworks. This role involves leadership in the technical framework as well as engagement with stakeholders whom will utilize the interfaces and applications developed.
Serve as a principal technical leader within the Chief Innovation Strategy Organization (CSO) in McLean, VA. We aren't looking for senior engineers who simply manage tasks from a desk, we want technical authorities who architect visionary solutions, shape complex technical strategy, and set the highest standard for engineering excellence across a wide spectrum of software disciplines.
To maximize growth and ensure agility in changing markets, our leadership is evolving our structure and assembling our top talent into a unified engineering engine designed to outpace emerging challenges and drive sector-wide advancement. You will be a primary driver of that mission, charting technical pathways, leading high-impact initiatives, and mentoring the next generation of engineers.
You won't be weighed down by bureaucratic stagnation. You’ll join a culture that values bold technical risks, rewards creative engineering, and gives senior leaders the authority to shape the future of our technical and strategic software portfolio. Join a rapidly growing organization, scaling our agentic construction of capabilities, serving our customers and ourselves leveraging advanced frameworks. You will be important to continue evolving with and ahead of the market.
Role Description
We are hiring a Senior Software Developer to act as a technical pillar across our multi-disciplinary portfolios. This position is a senior role focused on AI Harness Development. You will own the architecture and execution of high-visibility systems, providing leadership across:
- Advanced UX Designs, Agentic Framework Implementation Models and Tools
- Advanced System Architecture & Full-Stack Software Engineering
- Complex AI/ML Service Integration & Agentic System Design
- Integration with MCP Frameworks for Data, Identity, Cost and Cyber Security Controls
- Enterprise-Grade Cloud Infrastructure, Automation, and CI/CD Pipelines
- Multi-Platform Enterprise Integration (ServiceNow, Salesforce, Splunk, Custom Ecosystems)
Responsibilities
- Architect Complex Systems – Design and implement comprehensive end-to-end architectures, ensuring seamless, high-performance integration between modern UI frameworks, backend services, cloud infrastructure, and advanced AI/ML capabilities.
- Drive Technical Vision with Leadership – Partner closely with PIs, strategic planners, and executive stakeholders to conceptualize, scope, and execute strategic technical pathways.
- Elevate Engineering Standards – Champion Agentic Frameworks in the CI/CD workflow. Develop automated testing, application performance optimization, token optimization, anti-hallucination harnesses and rigorous software practices to eliminate deployment friction and technical debt.
- Mentor & Cultivate Technical Talent – Actively mentor junior and mid-level developers, fostering expertise in AI-native engineering practices, modern tool adoption, and clean architectural patterns.
- Lead Innovation & Execution – Oversee technical execution from initial prototyping to operational deployment, resolving complex cross-domain bottlenecks with authority and precision.
What We Are Looking For – The Essentials
- Leadership in AI Harness Engineering – Leadership and operational models for solving modern AI (LLM, SLM, Harness) design addressing complex government workflows, addressing complex data engineering requirements as well as broad compliance requirements.
- Mastery of Software Engineering Principles – Deep expertise in multiple languages (such as Python, JavaScript/TypeScript, Rust, Java, or C++), microservices architecture, cloud to embedded and distributed systems design.
- Proven Architecture Experience – Extensive hands-on background integrating complex software components, cloud services, databases, and third-party APIs or Large & Small Language Model worflows.
- DevSecOps & Infrastructure Leadership – Comprehensive understanding of containerization (Docker, Kubernetes), Infrastructure-as-Code (Terraform, Ansible), and enterprise-grade CI/CD pipelines. Design for accreditation and deployment is key.
- Collaborative Leadership – Demonstrated success in technical teams, partnering with research or product stakeholders, and elevating the capabilities of surrounding engineers.
- Track Record – History of success in teams, delivery of viable systems from start to finish, adapting to changing tools and frameworks. Be prepared to share lessons learned.
A Modern Mindset – Your Growth Edge
- AI-First Technologist – You operate at the frontier of AI-assisted engineering and agentic workflows, utilizing LLMs and modern automation frameworks to redefine how enterprise software is conceptualized and built.
- Adaptable Visionary – Unfazed by ambiguity; capable of taking a high-level strategic objective and breaking it down into an actionable, highly scalable engineering roadmap.
Character & Culture
- Mission-Driven & Competitive – Deeply committed to delivering bulletproof technical solutions that solve critical operational challenges.
- Generous Team Member – Earns respect through technical depth, emotional intelligence, and an active commitment to mentoring and uplifting others.
Working Conditions
- Onsite in McLean, VA. Flexible work and time options.
- Occasional travel to other BAE Systems sites, partner facilities, and stakeholder meetings.
- Reports to: Sr. Director, Capability Development.
Required Education, Experience, & Skills
- Bachelor’s degree in Engineering, Computer Science, related field or equivalent high-impact practical experience.
- 6+ years of professional software development experience, with a proven track record of designing and delivering complex, distributed software systems.
- Experience executing rapid-cycle R&D in defense, aerospace, advanced prototyping, or adjacent mission areas.
- Strong understanding of AI/ML frameworks, autonomy, cyber engineering, and human-machine teaming.
- Ability to obtain and maintain a security clearance. TS/SCI Preferred.
Preferred Education, Experience, & Skills
- Deep fluency in at least two of the following: Software Engineering, AI Harness Engineering, Cybersecurity, Systems Engineering, Hardware/Firmware/Embedded Systems.
Pay Information
Full-Time Salary Range: $153377 - $260743
Please note: This range is based on our market pay structures. However, individual salaries are determined by a variety of factors including, but not limited to: business considerations, local market conditions, and internal equity, as well as candidate qualifications, such as skills, education, and experience.
Employee Benefits: At BAE Systems, we support our employees in all aspects of their life, including their health and financial well-being. Regular employees scheduled to work 20+ hours per week are offered: health, dental, and vision insurance; health savings accounts; a 401(k) savings plan; disability coverage; and life and accident insurance. We also have an employee assistance program, a legal plan, and other perks including discounts on things like home, auto, and pet insurance. Our leave programs include paid time off, paid holidays, as well as other types of leave, including paid parental, military, bereavement, and any applicable federal and state sick leave. Employees may participate in the company recognition program to receive monetary or non-monetary recognition awards. Other incentives may be available based on position level and/or job specifics.
About BAE Systems, Inc.
BAE Systems, Inc. is a defense, aerospace, and security company headquartered in Falls Church, Virginia, with more than 40,000 employees worldwide. We serve, supply, and protect those who protect us. As one of the most geographically diverse international defense companies, we deliver a full range of products and services spanning air, land, maritime, space, and cyber, including advanced electronics, intelligence solutions, and dedicated customer support.
Our culture is performance-driven and values-led, built on curiosity, creativity, and collaboration. Purpose isn't a tagline here. It's what drives us. We're developing breakthrough technologies that defend national security and make a lasting impact on our communities and our planet. When you join BAE Systems, you're not just building a career. You're contributing to a mission that truly matters.
This position will be posted for at least 5 calendar days. The posting will remain active until the position is filled, or a qualified pool of candidates is identified.
Salary Context
This $153K-$260K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At BAE Systems USA, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $153K to $260K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
BAE Systems USA AI Hiring
BAE Systems USA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in McLean, VA, US. Compensation range: $260K - $260K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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