Interested in this AI/ML Engineer role at Johns Hopkins University Applied Physics Laboratory?
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Description
Are you an organized individual who is passionate about exploring rapidly evolving technologies, particularly AI, to solve complex technical problems?
Do you build strong relationships and collaborate across organizations while continuously looking for opportunities to improve services, processes, and customer outcomes? If so, we’re looking for someone like you to join our team at APL.
We are seeking an Enterprise AI Service Manager to lead the operational management, evolution, and adoption of our enterprise AI applications platforms and services. These capabilities enable staff across the Lab to safely and effectively leverage AI—including generative AI—to accelerate research, engineering, and mission impact.
As a member of our team, you will contribute to the overall success of APL’s AI ecosystem by supporting engineers, scientists, and staff who are using AI to address critical challenges for our nation. Your excellent organizational, communication, and problem‑solving skills will help ensure AI services are secure, reliable, and scalable, and that governance and guardrails are in place to support responsible use. As AI Service Manager you will:* Oversee day\-to\-day operations of enterprise AI platforms and services, including monitoring health, usage, and support queues.
- Establish and maintain support models and service processes, coordinate incidents and escalations, and partner with technical teams to ensure reliable and scalable delivery.
- Partner with the AI Program Manager and related service owners to define, document, and evolve enterprise AI service offerings and roadmaps.
- Evaluate new capabilities and enhancement requests, recommend priorities, and initiate projects that advance platform capabilities and integrations.
- Serve as a primary liaison between AI enterprise applications teams and stakeholders across the organization, gathering and representing the voice of the customer in planning and decisions.
- Communicate updates and roadmaps, enable help desk support, and deliver presentations, demos, and briefings to drive effective AI adoption.
- Coordinate with cybersecurity, privacy, and governance teams to assess platform risks, proposed changes, and new use cases.
- Support responsible AI governance and ensure capabilities align with security, operational, and business requirements while balancing innovation with risk management.
- Manage AI platform vendors and service providers, coordinating contracts, licensing, renewals, and account administration. Represent organizational needs, escalate enhancement requests, and pursue pilots, early access programs, and strategic partnerships that shape future platform capabilities.
- Develop and manage service budgets and expenditures while overseeing software licensing, subscriptions, and utilization.
- Support chargeback and cost recovery processes, monitor service costs, and recommend strategies to maximize value, efficiency, and sustainable growth.
- Support the development and execution of enterprise AI strategy and service roadmaps by providing operational and strategic recommendations.
- Monitor emerging industry trends, application regulations, evolving AI capabilities, and customer needs, and foster collaboration across technical, operational, and business teams to advance AI adoption.
Qualifications You meet our minimum qualifications for the job if you...
- Possess a Bachelor's degree in an IT\-related or engineering field
- Have 8\+ years’ experience in a technical IT role, including Service Management or Project leadership
- Have 5\+years of project or people leadership
- Demonstrate experience managing enterprise technology platforms, digital services, or SaaS offerings.
- Have experience working across technical, operational, security, and business stakeholder groups.
- Have experience managing vendor relationships and software licensing agreements to include partnering with procurement to evaluate vendors, manage licensing, and navigate evolving vendor product models and cost structure.
- Have experience managing operational processes, incorporating governance practices, and developing service documentation.
- Demonstrate ability to balance customer needs, operational realities, risk considerations, and strategic priorities.
- Possess strong analytical, organizational, and financial management skills.
- Are able to obtain a Secret level security clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship.
You'll go above and beyond our minimum requirements if you...
- Possess a graduate level degree in IT\-related or engineering field
- Have experience with enterprise AI platforms and generative AI technologies.
- Have experience with cloud services, API platforms, and AI infrastructure.
- Demonstrate strong knowledge of IT service management frameworks and practices.
- Have experience supporting enterprise\-scale technology adoption initiatives.
- Have worked within research, engineering, government, or technology companies.
- Are comfortable navigating ambiguity and rapidly evolving technologies.
- Demonstrate strong communication skills, including presenting briefings and translating technical concepts for executive and non\-technical audiences.
- Can balance innovation with governance and operational excellence.
About Us Why Work at APL?
The Johns Hopkins University Applied Physics Laboratory (APL) brings world\-class expertise to our nation’s most critical defense, security, space and science challenges. While we are dedicated to solving complex challenges and pioneering new technologies, what makes us truly outstanding is our culture. We offer a vibrant, welcoming atmosphere where you can bring your authentic self to work, continue to grow, and build strong connections with inspiring teammates.
At APL, we celebrate our differences of perspectives and encourage creativity and bold, new ideas. Our employees enjoy generous benefits, including a robust education assistance program, unparalleled retirement contributions, and a healthy work/life balance. APL’s campus is located in the Baltimore\-Washington metro area. Learn more about our career opportunities at https://www.jhuapl.edu/careers.
All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, genetic information, veteran status, occupation, marital or familial status, political opinion, personal appearance, or any other characteristic protected by applicable law. APL is committed to providing reasonable accommodation to individuals of all abilities, including those with disabilities. If you require a reasonable accommodation to participate in any part of the hiring process, please contact [email protected].
The referenced pay range is based on JHU APL’s good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level with consideration for internal parity. For salaried employees scheduled to work less than 40 hours per week, annual salary will be prorated based on the number of hours worked. APL may offer bonuses or other forms of compensation per internal policy and/or contractual designation. Additional compensation may be provided in the form of a sign\-on bonus, relocation benefits, locality allowance or discretionary payments for exceptional performance. APL provides eligible staff with a comprehensive benefits package including retirement plans, paid time off, medical, dental, vision, life insurance, short\-term disability, long\-term disability, flexible spending accounts, education assistance, and training and development. Applications are accepted on a rolling basis.
Minimum Rate
$105,000 Annually
Maximum Rate
$290,000 Annually
Salary Context
This $105K-$290K range is above 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 Johns Hopkins University Applied Physics Laboratory, 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 $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 ($197K) sits 10% below the category median. Disclosed range: $105K to $290K.
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.
Johns Hopkins University Applied Physics Laboratory AI Hiring
Johns Hopkins University Applied Physics Laboratory has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Laurel, MD, US. Compensation range: $245K - $290K.
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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