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About This Role
Basic Qualifications :
Requires a Bachelor’s degree in Software Engineering, Systems Engineering, Computer Science, or a related Engineering, Science, Technology, or Mathematics field, plus a minimum of 8 years of relevant experience; or a Master’s degree in a related field plus a minimum of 6 years of relevant experience. CLEARANCE REQUIREMENTS:
Department of Defense Secret security clearance is required at time of hire. Applicants selected will be subject to a U.S. Government security investigation and must meet eligibility requirements for access to classified information. Due to the nature of work performed within our facilities, U.S. citizenship is required.
Responsibilities for this Position:
General Dynamics Mission Systems is seeking an experienced Program Analyst with a software or systems engineering background to join the AI Reliability Engineering (AIRE) team within DLA. This is not a traditional analyst role — you are an engineer who has led large, complex project teams and now brings that execution discipline to a team that is building the future of AI\-driven enterprise software at GDMS.
Reporting directly to the AIRE Senior Manager, you will serve as the operational backbone of a high\-performing engineering team. You will combine your engineering expertise with program management discipline to drive schedule execution, cost\-of\-labor tracking, day\-to\-day project oversight, and Agile facilitation across all AIRE initiatives. This role is designed so that a single position multiplies value across the entire program — you are the person who keeps the trains running while the engineers build.
Your engineering background is essential. You will participate in technical discussions, understand architectural trade\-offs, translate engineering complexity into leadership\-ready reporting, and earn the trust of a deeply technical team. Your experience managing project teams of 20 or more members means you understand how to coordinate complex, interdependent work streams at scale.Key Responsibilities:
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- Schedule \& Execution Management: Own the master schedule across all AIRE work streams. Track milestones, dependencies, and critical path items. Identify risks early and drive mitigation actions before they become blockers.
- Cost\-of\-Labor Tracking: Track cost\-of\-labor actuals against plan. Provide regular financial health updates to the Senior Manager and Director\-level leadership. Partner with the Business Administrative Manager on budget reporting and forecasting.
- Day\-to\-Day Project Oversight: Manage day\-to\-day project execution across engineering teams. Run standups, track action items, remove impediments, and ensure the team maintains velocity. You are the single point of accountability for knowing where every work stream stands at any given moment.
- Agile Facilitation: Serve as Scrum Master and Agile coach for the AIRE team. Facilitate sprint planning, retrospectives, backlog grooming, and demos. Tailor Agile ceremonies to the team’s needs — rigorous enough to maintain discipline, lean enough to avoid ceremony for ceremony’s sake.
- Reporting \& Analytics: Create clear, actionable reports for leadership that translate engineering progress into business terms. Summarize team performance, resource utilization, risk posture, and delivery confidence. You bridge the gap between what engineers know and what leadership needs to hear.
- Resource Coordination: Coordinate hiring actions, onboarding logistics, equipment provisioning, and team operations alongside the Business Administrative Manager. Ensure new team members are productive from day one.
- Technical Engagement: Leverage your engineering background to participate in technical discussions, understand system architecture decisions, and provide informed input on feasibility, risk, and sequencing. You are not a passive note\-taker — you are an engineer who speaks the team’s language.
- Stakeholder Coordination: Act as a cross\-functional liaison with Finance, HR, IT, and other enterprise stakeholders. Clear roadblocks, streamline processes, and ensure the AIRE team’s operational needs are met.
Why This Role Matters
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The AIRE team operates like a startup within a world\-class enterprise. The Senior Manager and Lead Software Architect drive the technical and people strategy. The Program Analyst is what makes execution repeatable and visible. Without this role, a fast\-moving engineering team risks losing track of commitments, costs, and coordination. With it, the team delivers on time, on budget, and with full leadership visibility.Required Qualifications
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- Bachelor’s degree in Software Engineering, Systems Engineering, Computer Science, or a related Engineering, Science, Technology, or Mathematics field, plus a minimum of 8 years of relevant experience; or Master’s degree plus a minimum of 6 years of relevant experience.
- Hands\-on software or systems engineering background with direct development or engineering execution experience.
- Demonstrated experience managing or leading project teams of 20 or more members in a technical engineering environment.
- Proven experience working on Agile programs, including serving as Scrum Master, Agile lead, or equivalent facilitation role.
- Experience with schedule management, cost tracking, and project reporting for engineering teams.
- Department of Defense SECRET security clearance at time of hire.
- U.S. citizenship required.
Preferred Qualifications
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- Prior experience as a software engineer, systems engineer, or technical lead before transitioning into program management or analyst roles — you built software before you managed the building of it.
- Experience with Agile tools and frameworks (Jira, Azure DevOps, SAFe, Scrum, Kanban) in a production engineering environment.
- Certified Scrum Master (CSM), SAFe Agilist (SA), or PMP certification.
- Familiarity with SRE practices, DevOps pipelines, CI/CD, and modern cloud\-native development workflows.
- Experience supporting fast\-paced, innovative teams in a matrixed defense or aerospace environment.
- Strong analytical skills with the ability to create clear, actionable reports and dashboards for leadership.
- Exceptional organizational and communication skills — you can context\-switch between a technical deep\-dive and an executive summary without losing fidelity.
- Experience with AI/ML development programs, enterprise application modernization, or legacy system replacement initiatives.
- Identifies opportunities to apply AI for continuous improvement and innovation.
Salary Note: This estimate represents the typical salary range for this position based on experience and other factors (geographic location, etc.). Actual pay may vary. This job posting will remain open until the position is filled. Combined Salary Range: USD $125,061\.00 \- USD $135,320\.00 /Yr. Company Overview:
General Dynamics Mission Systems (GDMS) engineers a diverse portfolio of high technology solutions, products and services that enable customers to successfully execute missions across all domains of operation. With a global team of 12,000\+ top professionals, we partner with the best in industry to expand the bounds of innovation in the defense and scientific arenas. Given the nature of our work and who we are, we value trust, honesty, alignment and transparency. We offer highly competitive benefits and pride ourselves in being a great place to work with a shared sense of purpose. You will also enjoy a flexible work environment where contributions are recognized and rewarded. If who we are and what we do resonates with you, we invite you to join our high\-performance team!
Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans
Salary Context
This $125K-$135K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At General Dynamics Mission Systems, 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. This role's midpoint ($130K) sits 28% below the category median. Disclosed range: $125K to $135K.
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.
General Dynamics Mission Systems AI Hiring
General Dynamics Mission Systems has 7 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Pittsfield, MA, US, Remote, US. Compensation range: $115K - $222K.
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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