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About This Role
RealmOne was built on the principle that people matter first and foremost. We believe in providing a strong work/life balance by investing in our employees and encouraging professional and personal growth. We do this by offering exceptional benefits, flexible schedules, and the tools necessary to achieve success through paid training, mentoring, and the opportunity to work alongside top\-notch industry professionals.
We are searching for talented individuals who provide intelligence, engineering, and mission management expertise for the Government. This program will maximize the effectiveness and efficiency of our country’s most important missions both at home and abroad. If you are ready to support a high\-performing team that truly makes a difference, then come join us!
Position contingent upon contract award!
Job Description
- Combines deep data and analytics skills with strong business acumen to solve business problems by understanding, preparing, and analyzing data to predict emerging trends and provide recommendations to optimize business results. Responsibilities include working with business leaders to solve business problems by understanding, preparing, and analyzing data to predict emerging trends and provide recommendations to optimize business results. Skills include mathematical optimization, discrete\-event simulation, rules programming and predictive analytics. Expected to have knowledge and/or experience in the following skills with focus on data science: Data Science, Apache Ambari, MapReduce, Spark, Labmda, Resilient Distributed Dataset, Java, Zookeeper, Knox, Big Data, IBM BigInsights, Apache Hadoop, SQL, RDBMS, Python, Big SQL, BigSheets\+E5Big R, Text Analytics, GPFS, HDFS, Platform Symphony, Structured And Unstructured Data, Open Source, R, POSIX, Yarn, Sqoop, Flume, JSON, XML, NoSQL, HBase, Pig, Hive, Oozie, Apache Solr, JSqsh, Data Server Manager, AQL, Data Security, Data Governance, Networking, Neural Net.
Qualifications
- A Senior labor category has over 10 years of experience and a MA/MS degree. A Senior labor category typically works on high\-visibility or mission critical aspects of a given program and performs all functional duties independently. A Senior labor category may oversee the efforts of less senior staff and/or be responsible for the efforts of all staff assigned to a specific job.
Position requires an active Security Clearance.
Pay Range: 193,000 – 223,000
*The RealmOne pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Our approach to crafting offers considers various factors to establish an equitable and competitive compensation package. These considerations include, but are not limited to, the extent and intricacy of the role’s responsibilities, the candidate’s educational background, their work experience, and the specific competencies crucial for success in the role.*
*RealmOne Benefits:*
- *Healthcare Coverage \+ Insurance:* *Medical: Three (3\) rich healthcare options through CareFirst with 100% or majority company\-paid premiums. Tax\-advantaged health savings account available with generous employer contribution. Dental \+ Vision: 100% employer\-paid for employees and family, with a buy\-up option available.*
- *Retirement \+ Savings:* *401K – 10% TOTAL CONTRIBUTION – 5% safe harbor – 5% annual profit share (both immediately vested!).*
- *Paid Time Off \+ More:* *4 weeks starting PTO – 11 federal holidays \+ 2 floating holidays – Paid hours for company\-required training.*
- *Career Growth \+ Development:* *Access to FREE 24/7 learning via Udemy – Opportunities to participate in tech councils, industry initiatives, etc. – $7,500 annual Educational \& Professional Development Assistance.*
- *MORE BENEFITS…FOR EVERY LIFESTYLE!* *– Paid parental leave – Annual swag drops – Flexible work schedules \-Generous referral bonus program – Employee appreciation \+ family\-friendly corporate events …and much more.*
*ABOUT US*
- *RealmOne is a mid\-sized science and technology company dedicated to solving our customers’ toughest mission challenges.*
- *Headquartered in Columbia, MD., RealmOne supplies advanced cybersecurity, data science, and software engineering services and products to customers in the Government and commercial sectors.*
- *RealmOne delivers encompassing mission assurance and critical systems support to government customers across various U.S. locations to include Colorado, Georgia, Hawaii, Texas, Utah, and Virginia.*
- *RealmOne has won numerous awards, including Top Workplaces by the Baltimore Sun. Across more than 20 prime contracts, RealmOne is a premier innovator for the Government and Department of Defense, and our team is located across the United States.*
Salary Context
This $193K-$223K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At RealmOne, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $193K to $223K.
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.
RealmOne AI Hiring
RealmOne has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Columbia, MD, US. Compensation range: $119K - $223K.
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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