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About This Role
As a Sr. AI/ML Engineer II, you will design, build, deploy, and maintain production-ready AI and machine learning solutions, leveraging advanced ML techniques to support Retrieval-Augmented Generation (RAG) capabilities and drive mission-focused outcomes. In this role, you will serve as a trusted technical advisor, ensuring secure and effective system integration, while translating complex findings into actionable insights for diverse audiences across the organization.
We know that you can't have great technology services without amazing people. At MetroStar, we are obsessed with our people and have led a two-decade legacy of building the best and brightest teams. Because we know our future relies on our deep understanding and relentless focus on our people, we live by our mission: A passion for our people. Value for our customers.
If you think you can see yourself delivering our mission and pursuing our goals with us, then check out the job description below!
What you'll do:
- Serve as a hands-on technical lead for an AI/ML engineering team, providing technical guidance, architectural direction, and AI implementation strategy.
- Design, build, train, integrate, deploy, and maintain production-ready AI/ML models and applications in secure environments.
- Apply machine learning techniques to support retrieval augmented generation
- Present results to a diverse audience in presentation or report form
- Support architectural leadership, technical support, and advisement services to ensure system technologies are integrated and meeting the appropriate security requirements
- Communicate technical findings, architectural recommendations, and project progress to both technical and nontechnical stakeholders.
What you'll need to succeed:
- Minimum of 6 years of relevant experience in AI/ML engineering, data science, machine learning, or a related technical discipline.
- Bachelor's or Master's degree in a relevant field, or equivalent professional experience.
- Active TS/SCI clearance with CI polygraph.
- Extensive experience leveraging open-source AI/ML models, with the ability to evaluate and justify the use of proprietary models when appropriate.
- Strong hands-on experience designing, building, training, integrating, deploying, and maintaining AI/ML models and production applications.
- Experience developing agentic AI systems, including AI agents, orchestration workflows, and automated data-processing capabilities.
- Expert-level proficiency in at least two of the following AI/ML areas: Generative AI/LLMs, Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), or neural networks.
- At least 6 years experience with at least two following technologies: Python, TensorFlow, SciKit-Learn, Anaconda, Numpy, R, Keras, and Postgres
- At least 6 years of experience designing, implementing, and maintaining data architectures and services supporting AI/ML solutions, including model optimization, evaluation, and maintenance through Agile user stories and DevSecOps CI/CD pipelines.
- Expert-level experience designing, developing, and deploying AI/ML solutions in classified AWS cloud environments at Impact Level 6 (IL6) or higher.
- Strong analytical skills, including the ability to assess program and system performance, identify manual processes, and develop algorithms or automation solutions to improve efficiency.
- Strong experience designing, developing, deploying, and maintaining containerized AI/ML applications.
- Ability to work primarily onsite across designated DC Metro locations, including Tyson's Corner and Reston, VA, with occasional customer-site visit
SALARY RANGE: $180,000 - $205,000
The salary range for this position is determined based on qualifications, skills, and relevant experience. The final salary offered will be determined based on several factors including:
- The candidate's professional background and relevant work experience
- The specific responsibilities of the role and organizational needs
- Internal equity and alignment with current team compensation
- This role is also eligible for additional compensation, subject to the terms and policies of MetroStar, which may include:
- + Performance-based bonuses + Company-paid training and/or certifications + Referral bonuses
Application Deadline: Applications will be accepted on a rolling basis until the position is filled; candidates are encouraged to apply as early as possible for full consideration.
Additional Compensation: This role may also be eligible for bonuses and/or additional incentives based on individual and company performance.
Benefits: All full-time employees are eligible to participate in our benefits programs:
- Health, dental, and vision insurance
- 401(k) retirement plan with company match
- Paid time off (PTO) and holidays
- Parental Leave and dependent care
- Flexible work arrangements
- Professional development opportunities
- Employee assistance and wellness programs
Like we said, we are big fans of our people. That's why we offer a generous benefits package, professional growth, and valuable time to recharge. Learn more about our company culture code and benefits. Plus, check out our accolades.
Commitment to Non-Discrimination
All qualified applicants will receive consideration for employment based on merit and without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, status as a protected veteran, or any other status protected by applicable federal, state, local, or international law.
What we want you to know:
In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.
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Salary Context
This $180K-$205K 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 Metrostar 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($192K) sits 9% below the category median. Disclosed range: $180K to $205K.
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.
Metrostar Systems AI Hiring
Metrostar Systems has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Reston, VA, US. Compensation range: $205K - $205K.
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.
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