Interested in this AI/ML Engineer role at SimVentions?
Apply Now →Skills & Technologies
About This Role
Overview:
SimVentions is 100% employee-owned and proudly ranked among Virginia’s Best Places to Work year after year!
We are seeking a Junior/Mid-Level AI/ML Engineer specializing in Retrieval-Augmented Generation (RAG) to support the development of AI capabilities within an emerging MLOps platform. The engineer will help architect and implement reusable RAG pipelines that transform domain-specific documents and knowledge repositories into searchable, AI-enabled resources.
The role will involve developing document ingestion and processing workflows, integrating embedding models and vector databases, implementing retrieval and LLM-based generation capabilities, and evaluating the quality and reliability of AI-generated responses.
The engineer will work alongside software developers, AI/ML engineers, and systems architects to integrate these capabilities into a scalable, secure platform supporting multiple customer projects.
Travel: Minimal CONUS and local travel required Clearance:
An interim Secret Clearance is required for this position. Applicants selected will be subject to a security investigation and must meet eligibility requirements for access to classified information. US Citizenship is required to obtain a clearance. Requirements:
- Hands on experience with software development languages like GO, Python, Javascript, and Vue.js
- Understanding of data storage and retrieval utilizing databases, S3 storage containers and/or VAST storage environments
- AI/ML Knowledge: Entry/Foundational knowledge of ML concepts, intelligent automation capabilities, and the ML model lifecycle (training, validation, deployment, monitoring)
- Understanding on the utilization of CPU and GPU for Machine Learning and Model Training
- Experience with integrating open source, commercial, and internally developed toolsets using Software Development Kits (SDKs) and Application Programming Interfaces (APIs)
Responsibilities:
The software developer will work within the current development environment and toolsets to expand and enhance automation capabilities within the Machine Learning Operations (MLOPS) ecosystem. In addition, the software developer will work to automate model training processes to allow efficient repeatability, utilize data and model requirements to design architecture to scale, and inject configuration management of the data sources and model output. Preferred Skills and Experience:
- Familiarity or experience with components of a RAG pipeline for domain-based knowledge queries
- Familiarity with Docker and containerized applications
- Familiarity with MongoDB, PostgressSQL, and common SQL and Graph database interactions
Education: A Bachelor of Science (BS) degree from an accredited college or university in an engineering, scientific, or technical discipline is required. Compensation:
Compensation at SimVentions is determined by a number of factors, including, but not limited to, the candidate’s experience, education, training, security clearance, work location, skills, knowledge, and competencies, as well as alignment with our corporate compensation plan and contract specific requirements.
The projected annual compensation range for this position is $79,000 - $115,000 (USD). This estimate reflects the standard salary range for this position and is just one component of the total compensation package that SimVentions offers. Benefits:
At SimVentions, we’re committed to supporting the total well-being of our employees and their families. Our benefit offerings include comprehensive health and welfare plans to serve a variety of needs.
We offer:
- Medical, dental, vision, and prescription drug coverage
- Employee Stock Ownership Plan (ESOP)
- Competitive 401(k) programs
- Retirement and Financial Counselors
- Health Savings and Health Reimbursement Accounts
- Flexible Spending Accounts
- Life insurance, short- & long-term disability
- Continuing Education Assistance
- Paid Time Off, Paid Holidays, Paid Leave (e.g., Maternity, Paternity, Jury Duty, Bereavement, Military)
- Third Party Employee Assistance Program that offers emotional and lifestyle well-being services, to include free counseling
- Supplemental Benefit Program
Why Work for SimVentions?:
SimVentions is about more than just being a place to work with other growth-orientated technically exceptional experts. It’s also a fun place to work. Our family-friendly atmosphere encourages our employee-owners to imagine, create, explore, discover, and do great things together. Support Our Warfighters
SimVentions is a proud supporter of the U.S. military, and we take pride in our ability to provide relevant, game-changing solutions to our armed men and women around the world. Drive Customer Success
We deliver innovative products and solutions that go beyond the expected. This means you can expect to work with a team that will allow you to grow, have a voice, and make an impact.
Get Involved in Giving Back
We believe a well-rounded company starts with well-rounded employees, which is why we offer diverse service opportunities for our team throughout the year. Build Innovative Technology
SimVentions takes pride in its innovative and cutting-edge technology, so you can be sure that whatever project you work on, you will be having a direct impact on our customer’s success. Work with Brilliant People
We don’t just hire the smartest people; we seek experienced, creative individuals who are passionate about their work and thrive in our unique culture. Create Meaningful Solutions
We are trusted partners with our customers and are provided challenging and meaningful requirements to help them solve. Employees who join SimVentions will enjoy additional perks like:* Employee Ownership: Work with the best and help build YOUR company!
- Family focus: Work for a team that recognizes the importance of family time.
- Culture: Add to our culture of technical excellence and collaboration.
- Dress code: Business casual, we like to be comfortable while we work.
- Resources: Excellent facilities, tools, and training opportunities to grow in your field.
- Open communication: Work in an environment where your voice matters.
- Corporate Fellowship: Opportunities to participate in company sports teams and employee-led interest groups for personal and professional development.
- Employee Appreciation:Multiple corporate events throughout the year, including Holiday Events, Company Picnic, Imagineering Day, and more.
- Founding Partner of the FredNats Baseball team: Equitable distribution of tickets for every home game to be enjoyed by our employee-owners and their families from our private suite.
- Food: We have a lot of food around here!
FTAC
Salary Context
This $79K-$115K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At SimVentions, 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 $211,950 based on 13,048 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $111,000. This role's midpoint ($97K) sits 54% below the category median. Disclosed range: $79K to $115K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
SimVentions AI Hiring
SimVentions has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Dahlgren, VA, US, Dahlgren, VA. Compensation range: $115K - $115K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.