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About This Role
APPLICATION INSTRUCTIONS:
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- CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply here, apply internally through Workday.
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- CURRENT PENN STATE STUDENT (not employed previously at the university) and seeking employment with Penn State, please login to Workday to complete the student application process. Please do not apply here, apply internally through Workday.
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- If you are NOT a current employee or student, please click “Apply” and complete the application process for external applicants .
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Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants .
POSITION SPECIFICS
We are searching for a talented, experienced, organized, detail\-oriented, and highly motivated AI/ML Engineer to join our Independent Verification and Validation (IV\&V) and Data Curation Center of Excellence in the Information Science Division within the All\-Domain Analytics and Signatures Office (A2SO) at the Applied Research Laboratory (ARL). This team serves as the foundation for effective machine learning—building, curating, and governing the data that directly determines whether AI/ML models succeed in operationally relevant environments. You will focus on hands\-on work with classified multi\-modal data, including imagery/FMV, geospatial and AIS tracks, signals and sensor data, and text\-based intelligence reports and develop pipelines, annotation frameworks, and quality assurance processes while gaining deep exposure to state\-of\-the\-art AI/ML research.
ARL is an authorized DoD Skillbridge partner and welcomes all transitioning military members to apply.
You will:
- Label, annotate, and curate multi\-modal datasets (imagery/FMV, geospatial, signals, text/documents) to support AI/ML model development and evaluation
- Design and implement data pipelines for ingestion, transformation, quality control, and cataloging of classified and open\-source data
- Develop and maintain annotation standards, labeling guidelines, and data governance processes to ensure consistency and reproducibility across projects
- Conduct IV\&V of datasets and model outputs to ensure data integrity, label accuracy, and fitness for intended use
- Collaborate with research scientists, algorithm developers, and program stakeholders to identify, acquire, and prioritize data requirements for specific AI/ML projects
- Build and maintain data catalogs, metadata schemas, and access\-controlled repositories that make curated datasets discoverable and accessible to the broader research team
- Employ Python, SQL, and annotation platforms (e.g., CVAT, Label Studio) to automate labeling workflows, perform exploratory data analysis, and develop quality metrics
- Document data provenance, lineage, and known limitations to support model evaluation, reproducibility, and responsible AI practices
- Collaborate within an Agile development environment as part of a large research team
Required Skills/Experience areas include:
- Bachelor's Degree in Computer Science, Data Science, Information Science, Engineering, or a related technical field
- Proficiency in Python and SQL for data manipulation, analysis, and pipeline development
- Familiarity with data annotation tools and workflows (e.g., CVAT, Label Studio, or equivalent)
- Understanding of machine learning concepts, including how data quality impacts model training, evaluation, and deployment
Preferred Skills/Experience areas include:
- Experience working with DoD or Intelligence Community data, workflows, or operational environments
- Familiarity with geospatial data formats (shapefiles, GeoJSON, AIS), imagery/FMV exploitation, or signals data processing
- Experience with data versioning tools (e.g., DVC, LakeFS) or data catalog platforms
- Knowledge of IV\&V methodologies and quality assurance practices for data\-driven systems
- Exposure to containerized development environments (Docker, Kubernetes) and cloud platforms (AWS, Azure)
- Current eligibility for access to classified information at the TS/SCI level or higher and may be subject to a government background investigation to upgrade clearance eligibility, if required
Your working location will be fully on\-site, located in State College, PA or Reston, VA . Questions related to flexible work should be directed to the hiring manager during the interview process.
MINIMUM EDUCATION, WORK EXPERIENCE \& REQUIRED CERTIFICATIONS
If filled as R\&D Engineer \- Artificial Intelligence (ARL) \- Advanced Professional, this position requires:\&\#xa;Bachelor's Degree \- Engineering or Science\&\#xa;5\+ years of relevant experience\&\#xa;Required Certifications:\&\#xa;None\&\#xa;\&\#xa;If filled as R\&D Engineer \- Artificial Intelligence (ARL) \- Intermediate Professional, this position requires:\&\#xa;Bachelor's Degree \- Engineering or Science\&\#xa;2\+ years of relevant experience\&\#xa;Required Certifications:\&\#xa;None\&\#xa;\&\#xa;If filled as R\&D Engineer \- Artificial Intelligence (ARL) \- Professional, this position requires:\&\#xa;Bachelor's Degree \- Engineering or Science\&\#xa;No prior relevant work experience required\&\#xa;Required Certifications:\&\#xa;None
ARL’s purpose is to research and develop innovative solutions to challenging scientific, engineering, and technology problems in support of the Navy, the Intel Community (IC), and other federal government customers.
FOR FURTHER INFORMATION on ARL, visit our website at www.arl.psu.edu .
BACKGROUND CHECKS/CLEARANCES
Employment with the University will require successful completion of background check(s) in accordance with University policies.
Notice regarding employment at the Applied Research Laboratory (ARL):\&\#xa;Employees must be eligible to obtain a government security clearance, participate in the ARL drug testing program, and comply with electronic and physical monitoring requirements applicable to federal contractors. ARL operates in a secure information environment involving Unclassified, Controlled Unclassified Information (CUI), and Classified information. Personal electronic devices brought onsite must be registered and may be restricted from certain areas. You must be a U.S. citizen to apply.
SALARY \& BENEFITS
The salary range for this position, including all possible grades, is $92,100\.00 \- $200,808\.00\.
Salary Structure \- Information on Penn State's salary structure
Penn State provides a competitive benefits package for full\-time employees designed to support both personal and professional well\-being. In addition to comprehensive medical, dental, and vision coverage, employees enjoy robust retirement plans and substantial paid time off which includes holidays, vacation and sick time. One of the standout benefits is the generous 75% tuition discount, available to employees as well as eligible spouses and children. For more detailed information, please visit our Benefits Page .
CAMPUS SECURITY CRIME STATISTICS
Pursuant to the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and the Pennsylvania Act of 1988, Penn State publishes a combined Annual Security and Annual Fire Safety Report (ASR). The ASR includes crime statistics and institutional policies concerning campus security, such as those concerning alcohol and drug use, crime prevention, the reporting of crimes, sexual assault, and other matters. The ASR is available for review here .
EEO IS THE LAW
Penn State is an equal opportunity employer and is committed to providing employment opportunities to all qualified applicants without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. If you are unable to use our online application process due to an impairment or disability, please contact 814\-865\-1473\.
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Salary Context
This $92K-$200K range is below 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 Penn State 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 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($146K) sits 32% below the category median. Disclosed range: $92K to $200K.
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.
Penn State University AI Hiring
Penn State University has 3 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer, Research Engineer. Based in University Park, PA, US. Compensation range: $200K - $277K.
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.
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