Interested in this AI/ML Engineer role at Visionist, INC?
Apply Now →About This Role
Active Top Secret (TS/SCI) clearance with polygraph is required.
Visionist has an exciting new, fully FUNDED opportunity for an AI Platform Lead on our largest PRIME contract. In this role, you will lead multiple engineering teams developing and deploying an enterprise LLM\-powered platform that supports analysts, engineers, and operators across a variety of mission domains. As the primary technical voice for the platform, you will work directly with stakeholders and end users to define requirements, guide implementation strategies, and drive adoption of AI capabilities across the organization. We are seeking a technical leader who stays at the forefront of AI technologies, can balance mission needs with technical innovation, and is passionate about delivering secure, scalable, and impactful solutions in a rapidly evolving environment.
For over 15 years, Visionist has been solving the Intelligence Community's toughest software and analysis challenges. As a 100% employee\-owned company, we prioritize our people—your job security is assured. We embed small engineering teams with analysts to rapidly identify and solve mission capability gaps playing a critical role in defending our nation’s cyber infrastructure \& providing expertise in malware analysis, attribution, mapping adversarial infrastructure, pen testing, and operational planning. Our open\-door leadership team fosters a supportive culture, where internal growth and promotion opportunities are the norm. Don’t just take our word for it—check out our 4\.8\-star review on Glassdoor. Join a company that feels like a family with regular happy hours, baseball games, activity clubs and more. Check us out at www.visionistinc.com.
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Your contributions are…
- Solicit requirements and architect an enterprise agentic platform designed to power intelligent, autonomous workflows
- Work directly with stakeholders, engineers, and cyber analysts to understand operational workflows and identify high\-impact automation opportunities
- Evaluate AI models, deployment architectures, and infrastructure options to determine the optimal balance of capability, performance, and cost
- Advise customers on AI investment strategies and resource allocation to maximize mission impact and operational effectiveness
- Stay current on emerging AI technologies, industry trends, and best practices, advocating for the adoption of innovative capabilities where appropriate
- Conduct outreach and training efforts to promote adoption of AI capabilities and best practices across the user community
- Lead and coordinate multiple engineering teams by defining requirements, developing technical solutions, assigning work, and tracking execution against objectives
Requirements for your new career…
- Bachelor's degree in a technical discipline. (Additional 4 years of experience may substitute degree)
- 12 years of experience in software development
- Experience building, leading, and mentoring engineering teams with diverse technical backgrounds
- Demonstrated experience engaging with customers and end users to gather requirements, advocate for change, and drive adoption of technical solutions
- Hands on experience building and maintaining production applications servicing hundreds to thousands of users
- Experience designing and implementing agentic workflows, AI\-powered applications, or multi\-agent orchestration systems
- Experience leveraging AI coding assistants for code generation, refactoring, debugging, and automation (Codex, ClaudeCode)
Benefits of becoming a Visionist: Your New Career
- We are a 100% employee\-owned company, so our employees see the benefit of their contributions and have a stake in our overall success!
- Competitive 15% retirement contribution! (5% 401K match \& 10% ESOP)
- 4 weeks paid time off that is never “use or lose”, 12 paid holidays, comp time, overtime, AND flexible work hours
- 80 hours of paid parental leave with an additional $8,000 supplemental payment upon returning from maternity
- Medical, dental, \& vision benefits for both individuals and families (those who waive medical benefits will receive an additional $4,160/year)
- Annual lifestyle bonus of $600 – use it towards gyms/fitness, new tech, or your HSA!
- Annual merit increases \& performance\-based bonuses
- Term life insurance, short\-term disability, \& long\-term disability
Salary range: $170,000 \- $240,000
Disclaimer: Salary for this position, along with additional compensation options, will be determined on an individual basis following the interview process, considering various factors such as years of experience, skills, education/certifications, contract specifications, market conditions, etc.
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Not a good fit? Check out our other opportunities: https://jobs.jobvite.com/visionist
Next steps: Apply online and one of our recruiters will reach out to you. We have a streamlined process of phone screen with a recruiter, interview with a Visionist team at our HQ in Columbia, MD, and that is all!
Interested in learning more about Visionist and the work we do? Check out our website! https://www.visionistinc.com/what\-we\-do
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*U.S citizenship required (green card holders and permanent residents are not eligible). Applicants selected will be required to obtain / maintain a government security clearance.*
*Visionist, Inc. is an Equal Opportunity / Protected Veterans / Individuals with Disabilities employer.*
Salary Context
This $170K-$240K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Visionist, INC, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($205K) sits 13% above the category median. Disclosed range: $170K to $240K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Visionist, INC AI Hiring
Visionist, INC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Laurel, MD, US. Compensation range: $240K - $240K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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