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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 self\-motivated Vulnerability Researcher to join our Cyberspace Operations Research Department of the Applied Research Laboratory (ARL) at Penn State University. The Cyberspace Operations Research Department provides technical expertise in adversarial tactics, techniques, and procedures (TTPs) to support system evaluation and provide security hardening support to various sponsors.
ARL is an authorized DoD SkillBridge partner and welcomes all transitioning military members to apply.
You will:
- Research cutting edge techniques and technologies for system exploitation ·
- Leverage expertise in reverse engineering, vulnerability research, and software development to address established needs and investigate solutions to emerging requirements
- Apply software engineering best practices to create proof\-of\-concept tools and configure test environments to perform demonstrations to sponsors and partners
- Assist the Cyberspace Operations Research Department Head in maintaining relationships with sponsors through on\-site technical discussions, preparation and presentation of technical materials, and project\-related correspondence
- Provide process enhancement for technical functionality evaluation, vulnerability analyses, cyberspace operations engineering, and penetration testing projects
- Apply project management principles and methods to the leadership of tasks or projects
- Provide guidance to lower\-level engineers to foster professional growth
Additional responsibilities for higher level position includes:
- Understand broad strategic objectives and contribute to them; nurture and maintain relationships with major customers/grantors of external research and development grant funding
- Initiate new project concepts and seek funding; develop technical proposals and make presentations to customers/grant sponsors
- Mentor department staff in the development technical, project, and business development skills
Required skills/experience areas include:
- Documented application of reverse engineering and vulnerability testing tools (e.g. Ghidra/IDA, Kali Linux, Angr/AFL\+\+, QEMU/Unicorn, etc)
- Reverse engineering, vulnerability research, malware analysis, and/or CNO tool development
- Computer networking fundamentals, Windows and \*NIX operating systems, X86(64\) and ARM or MIPS architectures
- Cyber operations architecture and software engineering practices
- Ability to express technical information clearly and concisely in documents and presentations for senior leaders to successfully comprehend and leverage for decision making
- Active government security clearance at TS/SCI level with eligibility for special accesses
Preferred skills/experience areas include:
- Cybersecurity exploit development and test environment configuration
- Cybersecurity penetration testing / cybersecurity Red Team testing / white hat hacking
- Military cyberspace operations or computer network operations
- Demonstrated history of successful project initiation efforts
- Agile software development methodology and tools
Atlassian products (e.g., JIRA, Confluence)
*
Your working location will be fully on\-site located at in State Col lege, PA . Options exist for occasional hybrid of on\-site/work from home based on project\-dependent ability. This position will require periodic travel to remote locations in support of testing as part of a small team of researchers, engineers, and technologists.
MINIMUM EDUCATION, WORK EXPERIENCE \& REQUIRED CERTIFICATIONS
If filled as Cyber Security Engineer (ARL) \- Senior Professional, this position requires:\&\#xa;Bachelor's Degree\&\#xa;6\+ years of relevant experience; or an equivalent combination of education and experience accepted\&\#xa;Required Certifications:\&\#xa;None\&\#xa;\&\#xa;If filled as Cyber Security Engineer (ARL) \- Advanced Professional, this position requires:\&\#xa;Bachelor's Degree\&\#xa;3\+ years of relevant experience; or an equivalent combination of education and experience accepted\&\#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 $127,080\.00 \- $277,200\.00\. This salary range includes an adjustment based on required geographic work location.
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 $127K-$277K range is below the median for Research Engineer roles in our dataset (median: $207K across 63 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 4,317 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Penn State University, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills in Demand for This Role
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $272,100 based on 227 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($202K) sits 26% below the category median. Disclosed range: $127K to $277K.
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 AI Engineering Manager ($244,000). 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 Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
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).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
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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