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About This Role
Responsibilities:
Noblis is seeking an AI/ML Engineer with a TS/SCI with Polygraph to support mission\-critical national security initiatives.
In this role, you will design, develop, and deploy advanced machine learning and AI solutions while building the scalable infrastructure needed to operationalize AI capabilities in secure, production environments.
Job Responsibilities:* Model Development \& Deployment
+ Design, develop, and containerize machine learning (ML) models using modern frameworks and tools, including PyTorch, Ray, Docker, and FastAPI.
+ Deploy, manage, and scale production ML workloads on Kubernetes.
+ Integrate AI/ML capabilities into full\-stack applications using Python\-based backend services and JavaScript frontend technologies.
+ Ensure model reliability, performance, and maintainability throughout the deployment lifecycle.
- Infrastructure \& Operations
+ Architect and implement cloud\-native ML infrastructure on AWS.
+ Develop and maintain DevOps and MLOps pipelines to streamline model development, testing, deployment, and monitoring.
+ Deploy and support AI/ML systems within secure, classified, and high side environments.
- Technical Leadership
+ Evaluate and integrate state\-of\-the\-art AI/ML models, frameworks, and emerging technologies to enhance mission capabilities and accelerate innovation
+ Architect scalable, resilient, and secure infrastructure to support evolving AI/ML workloads, production deployments, and mission\-critical requirements
+ Establish and champion best practices for production\-grade machine learning (ML) systems, including MLOps, security, observability, governance.
+ Provide technical guidance across AI/ML initiatives and engineering teams.
Required Qualifications:
- Active Top Secret/SCI (TS/SCI) clearance with a current Polygraph
- Bachelor’s degree with 3 years of related experience; OR Master's degree with 1 years of related experience; OR associate’s degree with 6 years of related experience; OR High School diploma/GED with 9 years of related experience
- Demonstrated experience deploying machine learning (ML) models to production, including large language models (LLMs)
- Demonstrated experience with machine learning (ML) frameworks and containerization technologies (e.g., PyTorch, Docker, and Kubernetes)
- Full\-stack software development experience using Python and JavaScript
- Working knowledge of AWS cloud services and infrastructure
- Demonstrated experience implementing MLOps and DevOps best practices
- U.S. Citizenship is required
Desired Qualifications:
- Proficiency in Python with hands\-on experience using leading machine learning frameworks, including TensorFlow, PyTorch, or scikit\-learn
- Experience designing and implementing end\-to\-end machine learning (ML) pipelines, including data preprocessing, feature engineering
- Familiarity with cloud\-based machine learning (ML) platforms such as AWS SageMaker, Azure Machine Learning, or Google Vertex AI
- Understanding of MLOps best practices, including model versioning, experiment tracking, model monitoring, and CI/CD for machine learning (ML) workflows using tools such as MLflow, Weights \& Biases, or DVC
- Ability to collaborate with cross\-functional teams including data engineers, product managers, and domain experts to translate business problems into ML solutions
- Experience working with large\-scale datasets and distributed computing frameworks such as Apache Spark or Dask to support efficient data processing and model training
- Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes) for scalable model serving and orchestration
- Strong communication skills with the ability to explain model behavior, trade\-offs, and results to both technical and non\-technical stakeholders
Overview:
Overview
Noblis and our wholly owned subsidiaries, Noblis ESI and Noblis MSD, take on some of the nation’s toughest challenges, delivering advanced solutions to our customers’ most critical missions. We bring together leading scientific, engineering, and management expertise in a culture grounded in objectivity and collaboration, ensuring our work creates lasting impact across federal missions.
We work with a broad range of government agencies in the defense, intelligence, and federal civilian sectors. Learn more and find opportunities at careers.noblis.org Why Work at Noblis
At Noblis, we share a passion for excellence and innovation, and we create an environment where people can do meaningful work while maintaining the balance that keeps them energized and fulfilled. We seek out individuals with a natural curiosity and desire to collaborate and learn. We believe our people are our greatest strength, and we consistently seek exceptionally skilled, mission‑driven professionals who care deeply about doing work that enriches lives and makes our nation safer.
Noblis has earned numerous workplace awards for our culture, our commitment to employee well‑being, and our dedication to meaningful, impactful work. We also maintain a drug‑free workplace. *Remote/hybrid status is subject to change based on Noblis and/or government requirements.*
Commitment to Non\-Discrimination:
All qualified applicants will receive consideration for employment without regard to race, color, ethnicity, sex, age, national origin, religion, physical or mental disability, pregnancy/childbirth and related medical conditions, veteran or military status, or any other characteristics protected by applicable federal, state, or local law.
If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact us.
EEO is the Law \| E\-Verify \| Right to Work
Total Rewards:
At Noblis we recognize and reward your contributions, provide you with growth opportunities, and support your total well\-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, and work\-life programs. Our award programs acknowledge employees for exceptional performance and superior demonstration of our service standards. Full\-time and part\-time employees working at least 20 hours a week on a regular basis are eligible to participate in our benefit programs. Other offerings may be provided for employees not within this category. We encourage you to learn more about our total benefits by visiting the Benefits page on our Careers site.
Compensation at Noblis is determined by various factors, including but not limited to, the combination of education, certifications, knowledge, skills, competencies, and experience, internal and external equity, location, clearance level, as well as contract\-specific affordability, organizational requirements and applicable employment laws. The projected compensation range for this position is based on full time status. For part time or on\-call staff, compensation is proportionately adjusted based on hours worked. While monetary compensation is important, it's just one component of Noblis’ total compensation package.
Posted Salary Range: USD $109,800\.00 \- USD $171,525\.00 /Yr.
Salary Context
This $109K-$171K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Noblis, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($140K) sits 36% below the category median. Disclosed range: $109K to $171K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Noblis AI Hiring
Noblis has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Arlington, VA, US, Reston, VA, US. Compensation range: $171K - $207K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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