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Affiliated Office Address
Washington, DC, United States
Requisition ID
121952
Date Created
July 28, 2026
Job Family
Data Management
Job Subfamily
Data Management
Job Function
Clerical/Administrative Support
Exempt Status
Non\-Exempt
Shift Type
Full Time
Schedule
8:30am\-5pm Monday \-Friday
Worksite
01\-DC:District of Columbia
Work Modality
Remote: Not on\-site or \< 10% of hours worked on\-site
Johns Hopkins’ first new academic division since 2007, the School of Government and Policy is anchored at the Hopkins Bloomberg Center in the nation’s capital. It draws on the university’s 150\-year research tradition to develop innovative, evidence\-based solutions and prepare the next generation of policy and public sector leaders to advance evidence\-based solutions for the policy and governance challenges that matter most.
We are seeking an *AI Evaluations Engineer* who will be working in a start\-up environment – creating processes and procedures, navigating unexpected obstacles, and adapting to change frequently. An agile mindset and flexible approach to daily responsibilities is needed as we define new ways of working within a well\-established university. We are seeking a solution\-focused person with a demonstrated ability to create new paths to achieve results.
The AI Evaluations Engineer is responsible for entering, maintaining, auditing, and processing sensitive data for the assigned area. The AI Evaluations Engineer is responsible for maximizing technological capabilities to reduce manual processes, preparing and processing ad\-hoc and scheduled reports, and recommending resources for process\-improvement tasks. The AI Evaluations Engineerwill run and resolve data anomalies/inconsistencies and generate reports.
Specific Duties \& Responsibilities
- Provide data support for the team, all associated end\-users, and members of senior management.
- Validate data and the accurate completion or routing of each reporting project based on pre\-determined processes.
- Act as the initial contact for data corrections, escalate as necessary, and execute approved corrections.
- Act as a resource and guide for clients and business partners regarding data and standardized business processes.
- Maintain complete and accurate records.
- Ensure the highest level of accuracy for data entered into the systems to ensure successful data audits.
- Audit data entry on a regular basis for accuracy and compliance.
- Prepare and present data metrics as needed.
- Create standard and/or ad\-hoc reports out of assigned systems to ensure compliance with local, state, and federal reporting guidelines.
- Participate in the creation of new queries and reports as needed.
- Initiate technology service requests to revise or develop existing or new modules for processing data or reports.
- Assist with the support of all aspects of the assigned data process as it relates to data requests, including but not limited to data availability, feasibility of requests, ad\-hoc reports, and technical support.
- Adhere to organizational and department\-specific safety and confidentiality policies and standards.
- Use functional and technical knowledge to provide end\-users with general guidance and informal training as needed.
- Assist with the creation of internal training tools and process guides for staff to maximize effective use of the applications and systems.
- Prepare and present workshops to users to explain system requirements and system procedures.
- May respond to technical issues and recommend changes to the existing data platform.
- Other duties as assigned.
Minimum Qualifications
- High School Diploma or graduation equivalent.
- Three years of administrative/academic related experience.
- Additional education may substitute for required experience and additional related experience may substitute for required education beyond a high school diploma/graduation equivalent, to the extent permitted by the JHU equivalency formula.
Classified Title: Data Specialist
Job Posting Title (Working Title): AI Evaluations Engineer (School of Government and Policy)
Role/Level/Range: ATO 37\.5/03/OF
Starting Salary Range: $21\.80 \- $37\.80 HRLY ($60,000 targeted; Commensurate w/exp.)
Employee group: Full Time
Schedule: 8:30am\-5pm Monday \-Friday
FLSA Status: Non\-Exempt
Location: Remote
Department name: Staff
Personnel area: School of Government \& Policy
*Total Rewards*
The referenced base salary range represents the low and high end of Johns Hopkins University’s salary range for this position. Not all candidates will be eligible for the upper end of the salary range. Exact salary will ultimately depend on multiple factors, which may include the successful candidate's geographic location, skills, work experience, market conditions, education/training and other qualifications. Johns Hopkins offers a total rewards package that supports our employees' health, life, career and retirement. More information can be found here: https://hr.jhu.edu/benefits\-worklife/.
*Education and Experience Equivalency*
Please refer to the job description above to see which forms of equivalency are permitted for this position. If permitted, equivalencies will follow these guidelines: JHU Equivalency Formula: 30 undergraduate degree credits (semester hours) or 18 graduate degree credits may substitute for one year of experience. Additional related experience may substitute for required education on the same basis. For jobs where equivalency is permitted, up to two years of non\-related college course work may be applied towards the total minimum education/experience required for the respective job.
*Applicants Completing Studies*
Applicants who do not meet the posted requirements but are completing their final academic semester/quarter will be considered eligible for employment and may be asked to provide additional information confirming their academic completion date.
*Background Checks*
The successful candidate(s) for this position will be subject to a pre\-employment background check. Johns Hopkins is committed to hiring individuals with a justice\-involved background, consistent with applicable policies and current practice. A prior criminal history does not automatically preclude candidates from employment at Johns Hopkins University. In accordance with applicable law, the university will review, on an individual basis, the date of a candidate's conviction, the nature of the conviction and how the conviction relates to an essential job\-related qualification or function.
*Diversity and Inclusion*
The Johns Hopkins University values diversity, equity and inclusion and advances these through our key strategic framework, the JHU Roadmap on Diversity and Inclusion.
*Equal Opportunity Employer*
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
*EEO is the Law*
https://www.eeoc.gov/sites/default/files/2023\-06/22\-088\_EEOC\_KnowYourRights6\.12ScreenRdr.pdf
*Accommodation Information*
If you are interested in applying for employment with The Johns Hopkins University and require special assistance or accommodation during any part of the pre\-employment process, please contact the Talent Acquisition Office at [email protected]. For TTY users, call via Maryland Relay or dial 711\. For more information about workplace accommodations or accessibility at Johns Hopkins University, please visit: https://accessibility.jhu.edu/.
*Vaccine Requirements*
Johns Hopkins University requires all faculty, staff, and students to receive the seasonal flu vaccine. Exceptions to the flu vaccine requirements may be provided to individuals for religious beliefs or medical reasons. Requests for an exception must be submitted to the JHU vaccination registry.
*The following additional provisions may apply, depending upon campus. Your recruiter will advise accordingly.*
The pre\-employment physical for positions in clinical areas, laboratories, working with research subjects, or involving community contact requires documentation of immune status against Rubella (German measles), Rubeola (Measles), Mumps, Varicella (chickenpox), Hepatitis B and documentation of having received the Tdap (Tetanus, diphtheria, pertussis) vaccination. This may include documentation of having two (2\) MMR vaccines; two (2\) Varicella vaccines; or antibody status to these diseases from laboratory testing. Blood tests for immunities to these diseases are ordinarily included in the pre\-employment physical exam except for those employees who provide results of blood tests or immunization documentation from their own health care providers. Any vaccinations required for these diseases will be given at no cost in our Occupational Health office.
Salary Context
This $43K-$76K range is in the lower quartile 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 Johns Hopkins University, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($60K) sits 72% below the category median. Disclosed range: $43K to $76K.
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.
Johns Hopkins University AI Hiring
Johns Hopkins University has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Baltimore, MD, US, Washington, DC, US. Compensation range: $76K - $96K.
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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