Interested in this AI/ML Engineer role at General Dynamics Mission Systems?
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About This Role
Basic Qualifications :
Bachelor's degree in Systems Engineering, or a related Science, Engineering or Mathematics field, plus a minimum of 5 years of relevant experience; or Master's degree, plus a minimum of 3 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:
ROLE AND POSITION OBJECTIVES:What You'll Own
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- System integrations. Design and build connections between the AI platform and enterprise systems — ERP (Oracle, IFS), data platforms (Snowflake), PLM, MES, CRM, and legacy databases. You figure out how to get data in and push results back.
- API design and development. Build RESTful and event\-driven APIs that expose enterprise data to AI services and deliver AI outputs to business applications. Clean interfaces, clear contracts, proper versioning.
- Data flow architecture. Design the data pipelines that move information between systems — batch and real\-time. Handle transformation, validation, and error recovery.
- Integration reliability. Build monitoring, alerting, and automated recovery for integration points. When upstream systems change, your layer adapts or fails gracefully — it does not silently corrupt data.
- Security and compliance. Ensure all data flows meet access control, audit, and compliance requirements. You work with Cyber to get it right, but the implementation is yours.
What You Won't Own
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- AI model development or prompt engineering — that's the AI Engineers' job
- Enterprise system administration — you integrate with systems, you don't manage them
- Platform architecture decisions — you implement the integration patterns the Lead Architect defines
What Makes This Role Different
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- You are not plugging in connectors. You are solving interoperability problems between systems spanning decades of technology — from modern cloud APIs to legacy databases with no documentation.
- Your integrations feed AI systems that make real business decisions. Data quality and reliability are not nice\-to\-haves — they directly affect whether the AI works.
- You will have direct access to enterprise system teams and the authority to define integration contracts. You are not waiting on a ticket queue.
Required Qualifications
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- Bachelor’s degree in Computer Science, Software Engineering, or a related field, plus 5 years of experience; or Master’s degree plus 3 years of experience
- Production experience building API integrations between enterprise systems — ERP, CRM, data warehouses, or similar. You have connected systems that weren't designed to work together.
- Strong development skills in Python and/or Java — you write integration code, not just configure middleware
- Experience with RESTful API design, event\-driven architectures, and data transformation pipelines
- Database proficiency — SQL, stored procedures, schema design. You are comfortable working with both modern data platforms and legacy relational databases.
- Experience with CI/CD pipelines, containerized deployment (Docker), and cloud platforms (AWS, Azure, or GCP)
- S. citizenship required. Department of Defense Secret security clearance is required at time of hire.
Preferred Qualifications
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- Experience integrating with Oracle E\-Business Suite, IFS, SAP, or similar enterprise ERP platforms
- Experience with data streaming technologies — Kafka, event buses, change data capture (CDC)
- Experience with Snowflake, Palantir Foundry, or enterprise data platforms
- Familiarity with SOA patterns, API gateways, and service mesh architectures
- Experience building integrations that serve AI/ML systems — you understand what AI services need from data (embeddings, structured context, real\-time access)
What Sets You Apart
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- You have connected systems that everyone said couldn't be connected. Legacy databases with no documentation, APIs with no specs, platforms with no support.
- You think about failure modes before you think about the happy path. Your integrations handle errors, not just data.
- You build integrations that other people can understand and maintain — clean code, clear contracts, proper logging.
- You care about data quality. You know that garbage in means garbage out, and you build validation into everything.
Details
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- Remote — 100% telework
- 9/80 schedule
- Defense industry experience is not required
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 $118,519\.00 \- USD $131,482\.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 $118K-$131K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($125K) sits 30% below the category median. Disclosed range: $118K to $131K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
General Dynamics Mission Systems AI Hiring
General Dynamics Mission Systems has 8 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Pittsfield, MA, US, Chantilly, VA, US. Compensation range: $115K - $271K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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