Interested in this AI/ML Engineer role at Sabel Systems Technology Solutions?
Apply Now →About This Role
Why Sabel Systems
=====================
Sabel Systems designs and delivers digital engineering ecosystems for DoD programs, enabling teams to connect data, systems, and personnel within secure, scalable environments. Our solutions empower programs to make faster decisions, close sustainment gaps, and meet the digital acquisition requirements.
Rather than simply proposing transformation, we implement and operate it at scale. Our method is clear: we orchestrate existing tools into a governing system of record, ensuring government ownership of infrastructure and traceable decisions. There’s no vendor lock, no proprietary baselines, and no need for ATO rebuilds during transitions.
Sabel Systems is redefining modern defense acquisition. If you seek to solve complex challenges at scale and want your work to directly accelerate military capability while reshaping the DoD’s approach to digital engineering, this is the place for you.*Complexity Simplified. Mission Amplified.*
What You’ll Do
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AI Product Development \& Maturation* Own strategy, design, and development of AI\-driven products and capabilities.
- Mature AI offerings through structured iteration, performance improvement, and clear lifecycle management.
- Implement governance around model development, evaluation, deployment, monitoring, and responsible AI.
- Ensure AI solutions add meaningful mission value and scale appropriately across product lines.
Enterprise Architecture \& Mission Alignment* Define the enterprise architecture strategy and establish reference architectures, standards, and patterns.
- Ensure technical direction provides interoperability, scalability, security, and consistent product experience.
- Translate mission objectives, operational requirements, and customer challenges into architectural principles and product capabilities.
- Continuously evaluate how product architecture enables end\-user effectiveness and mission\-level outcomes.
Roadmap \& Growth Alignment* Map new customer opportunities to product capabilities and architectural investment needs.
- Ensure product evolution reflects demand signals, market trajectory, and growth strategy.
- Guide platform modernization through evaluation of emerging technologies, frameworks, and patterns; prioritize investments based on strategic value, mission impact, and reuse potential.
- Partner with growth, capture, and product teams to ensure technology investments anticipate future customer solution patterns.
Digital Engineering Integration* Integrate digital engineering principles such as MBSE, digital thread, and digital twins into product architectures.
- Define how engineering models and authoritative data environments interact with product functionality and AI features.
- Promote lifecycle traceability, automation, and engineering rigor across product development.
Governance, Alignment \& Talent* Lead architecture reviews, decision boards, and governance processes; enforce quality, consistency, security, compliance, and interoperability across all products.
- Build the connective framework between Product Architecture (roadmap, technology direction) and Solutions Architecture (opportunity shaping, customer\-facing design); equip Solutions Architects with reusable patterns and architectural guardrails.
- Ensure pursuit architectures conform to enterprise standards and inform future product investments.
- Provide architectural leadership during customer engagements, demos, and proposals; translate complex architectural concepts into clear narratives for executives, technical teams, and customers.
- Develop frameworks and systems to grow architects and engineers and strengthen architectural discipline across the team.
What You Bring
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### Required
- US Citizen
- Bachelor's degree in Engineering, Computer Science, Data Science, or related field (equivalent experience considered)
- 10\+ years of experience in enterprise or solution architecture, including 3\+ years leading AI/ML product or platform initiatives
- Proven experience delivering AI\-enabled products and maturing technical capabilities over time
- Expertise in cloud\-native architecture and distributed systems
- Ability to lead across product, engineering, growth, and customer\-facing teams
- Excellent communication skills and ability to translate deeply technical concepts for diverse audiences
### Preferred
- Experience with MBSE, digital thread, digital twin, PLM, or engineering lifecycle transformation
- Enterprise architecture certification (TOGAF, DoDAF, SAFe Architect)
- Experience in regulated or mission\-critical environments
- Familiarity with modern AI/ML frameworks, pipelines, and deployment patterns
Our Core Values* Bias for Action: Decisive. Purposeful. Agile. We move with the speed of relevance to drive impact and progress.
- Integrity: Respect. Ethics. Transparency. We do what’s right and earn lasting trust.
- Delivery Excellence: Customer\-obsessed. Mission\-focused. Quality\-driven. We deliver innovative outcomes that exceed expectations.
Our EVP Promise
Join Sabel Systems, where your contributions drive impact, your growth is continuously supported, and your well\-being is at the center of how we work. Together we can build the future with purpose. *Rewarding Impact. Building Futures Together.*Compensation
Compensation will be determined in partnership with the Hiring Manager and may vary based on factors such as contract and labor category alignment, relevant experience, skills, education, certifications or licenses, and geographic location.
Sabel Systems is committed to offering all employees a competitive benefits and compensation package that is comprehensive enough to meet their goals and needs. Our employees are our most valuable asset, and one of Sabel Systems largest financial investments is our benefits program. As a valued member of the organization, employees are provided with a host of benefits to include healthcare; financial assistance in the event of illness, injury, disability, loss of work, or death; health savings accounts; retirement plans; paid time off; paid holidays; education and training program reimbursement, to name a few.Equal Employment Opportunity
Sabel Systems is an equal opportunity employer. Our hiring decisions are based solely on qualifications, merit, and business need. We prohibit discrimination and harassment of any kind across all employment practices within our organization. Sabel Systems participates in the *E\-Verify Employment Verification Program.*Reasonable AccommodationSabel Systems is committed to providing equal employment opportunities and ensuring an accessible application process for all candidates. Applicants with disabilities who require reasonable accommodation to participate in the application or interview process are encouraged to contact us at [email protected] for assistance.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Sabel Systems Technology Solutions, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Sabel Systems Technology Solutions AI Hiring
Sabel Systems Technology Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Vienna, VA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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