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About This Role
Overview:
At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget.
Our Science \& Technology directorates include National Security, Earth and Biological Sciences, Physical and Computational Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus.
The Energy and Environment Directorate delivers science and technology solutions for the nation’s biggest energy and environmental challenges. Our more than 1,700 staff support the Department of Energy (DOE), delivering on key DOE mission areas including: modernizing our nation’s power grid to maintain a reliable, affordable, secure, and resilient electricity delivery infrastructure; research, development, validation, and effective utilization of renewable energy and efficiency technologies that improve the affordability, reliability, resiliency, and security of the American energy system; and resolving complex issues in nuclear science, energy, and environmental management.
The Electricity Infrastructure and Buildings Division, part of the Energy and Environment Directorate, is accelerating the transition to an efficient, resilient, and secure energy system through basic and applied research. We leverage a strong technical foundation in power and energy systems and advanced data analytics to drive innovation, transform markets, and shape energy policy.
Within this division, the Power System Modeling Group (PSMG) develops advanced simulation, analysis, and optimization tools to understand and enhance grid performance across all levels, from the bulk energy system to the distribution grid.
Rockstar Rewards:
Employees and their families are offered medical insurance, dental insurance, vision insurance, robust telehealth care options, several mental health benefits, free wellness coaching, health savings account, flexible spending accounts, basic life insurance, disability insurance\*, employee assistance program, business travel insurance, tuition assistance, relocation, backup childcare, legal benefits, supplemental parental bonding leave, surrogacy and adoption assistance, and fertility support. Employees are automatically enrolled in our company\-funded pension plan\* and may enroll in our 401 (k) savings plan with company match\*. Employees may accrue up to 120 vacation hours per year and may receive ten paid holidays per year.
- Research Associates excluded.
\*\*All benefits are dependent upon eligibility.
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Responsibilities:
PNNL seeks a creative and interdisciplinary Power Systems Research Engineer to conduct applied research in bulk electric system modeling, transmission planning, and grid analytics. The successful candidate will develop and apply power\-flow, contingency\-analysis, transfer\-analysis, and optimization methods to evaluate current and future transmission system performance under changing generation, load, and infrastructure conditions.
This role will contribute to nationally significant studies that support grid reliability, resilience, and energy transition objectives. Work will include building and using research\-grade software workflows; automating commercial and open\-source power\-system simulation environments; analyzing large\-scale transmission planning cases; and translating technical findings into actionable insights for DOE, utilities, system operators, and other stakeholders.
The staff member will work in interdisciplinary teams spanning power systems, software engineering, data science, and applied mathematics. The position is intended for a Level 2 researcher who can independently execute well\-defined technical tasks, contribute to study design, document methods and results, and grow into leadership of large tasks, projects or focused research components.
Key Responsibilities:* Develop, run, and interpret bulk power system studies, including steady\-state power flow, N\-1 and selected higher\-order contingency analysis, transfer capability analysis, congestion and overload assessment, and mitigation strategy evaluation.
- Design, implement, and maintain Python\-based workflows that automate power\-system simulation tools such as PowerWorld, PSS/E, PSLF, or comparable platforms for large\-scale planning and operations studies.
- Contribute to transmission planning studies involving renewable and offshore wind integration, interregional transfer capability, resource deliverability, transmission element upgrades, and grid resilience.
- Apply optimization, data\-driven modeling, and machine learning techniques to power\-system planning problems such as economic redispatch, optimal power flow, upgrade screening, dynamic model parameterization, and uncertainty analysis.
- Develop and validate reusable software tools, scripts, models, and visualization products that improve study reproducibility, scalability, and stakeholder understanding.
- Analyze large and complex power\-system datasets, including planning cases, contingency results, outage scenarios, time\-series measurements, and simulation outputs.
- Collaborate with interdisciplinary teams and external partners, including national laboratories, DOE sponsors, utilities, independent system operators, universities, and industry stakeholders.
- Prepare technical reports, presentations, peer\-reviewed publications, and sponsor\-facing briefings that clearly communicate methods, assumptions, findings, and limitations.
- Contribute to proposal development, scoping of new research directions, and the maturation of analytical capabilities aligned with DOE and PNNL mission areas.
- Work effectively in a team environment while independently managing assigned tasks, meeting project milestones, and following PNNL operational, safety, cybersecurity, and project management requirements.
Qualifications:
Minimum Qualifications:* BS/BA and 2 years of relevant experience \-OR\-
- MS/MA \-OR\-
- PhD
Preferred Qualifications:* Foundational knowledge of electric power systems, including transmission planning, power\-flow analysis, contingency analysis, renewable integration, or related grid operations concepts.
- Demonstrated programming experience in Python, MATLAB, Julia, C\+\+, C\#, or comparable languages for engineering analysis, automation, modeling, or data processing.
- Ability to work collaboratively in multidisciplinary research teams and communicate technical results through written reports, presentations, or publications.
- Graduate\-level training or research experience in electrical engineering with emphasis in power systems, transmission planning, renewable energy integration, machine learning, optimization, or related areas.
- Experience using or automating transmission analysis tools such as PowerWorld, PSS/E, PSLF, OPAL\-RT/RT\-LAB, MATLAB/Simulink, AVEVA PI, or comparable modeling and simulation platforms.
- Hands\-on experience conducting steady\-state contingency analysis, outage studies, transfer analysis, transient stability studies, or power\-flow studies on bulk electric system models.
- Experience developing software tools that automate power\-system simulations, manage large study runs, process simulation outputs, or create reproducible analysis pipelines.
- Knowledge of optimization methods relevant to power systems, including economic redispatch, optimal power flow, transmission upgrade screening, or related planning applications.
- Familiarity with inverter\-based resources, renewable integration, energy storage, microgrids, grid\-forming and grid\-following controls, or dynamic load modeling.
- Experience applying machine learning or deep learning methods to power\-system problems, such as dynamic model parameterization, forecasting, screening, or surrogate modeling.
- Experience with large\-scale regional or interregional transmission studies, offshore wind transmission studies, reliability or resilience analyses, or national\-scale grid modeling efforts.
- Ability to develop clear technical visualizations, one\-line diagrams, dashboards, or other communication products that support engineering decision\-making.
- Strong technical writing and communication skills, including experience preparing research presentations, sponsor deliverables, conference papers, theses, or peer\-reviewed manuscripts.
- Ability to thrive in an applied research environment with evolving study assumptions, multiple simultaneous projects, and collaboration across organizations.
Additional Information:
Not Applicable.
Testing Designated Position (TDP):
This is not a Testing Designated Position (TDP).
About PNNL:
Pacific Northwest National Laboratory (PNNL) is a world\-class research institution powered by a highly educated, diverse workforce committed to the values of Integrity, Creativity, Collaboration, Impact, and Courage. Every year, scores of dynamic, driven people come to PNNL to work with renowned researchers on meaningful science, innovations and outcomes for the U.S. Department of Energy and other sponsors; here is your chance to be one of them!
At PNNL, you will find an exciting research environment and excellent benefits including health insurance, and flexible work schedules. PNNL is located in eastern Washington State—the dry side of Washington known for its stellar outdoor recreation and affordable cost of living. The Lab’s campus is only a 45\-minute flight (or \~3 hour drive) from Seattle or Portland, and is serviced by the convenient PSC airport, connected to 8 major hubs.
Commitment to Excellence and Equal Employment Opportunity:
Our laboratory is committed to fostering a work environment where all individuals are treated with fairness and respect while solving critical challenges in fundamental sciences, national security, and energy resiliency. We are an Equal Employment Opportunity employer.
Pacific Northwest National Laboratory (PNNL) is an Equal Opportunity Employer. PNNL considers all applicants for employment without regard to race, religion, color, sex, national origin, age, disability, genetic information (including family medical history), protected veteran status, and any other status or characteristic protected by federal, state, and/or local laws.
We are committed to providing reasonable accommodations for individuals with disabilities and disabled veterans in our job application procedures and in employment. If you need assistance or an accommodation due to a disability, contact us at [email protected].
Drug Free Workplace:
PNNL is committed to a drug\-free workplace supported by Workplace Substance Abuse Program (WSAP) and complies with federal laws prohibiting the possession and use of illegal drugs.
If you are offered employment at PNNL, you must pass a drug test prior to commencing employment. PNNL complies with federal law regarding illegal drug use. Under federal law, marijuana remains an illegal drug. If you test positive for any illegal controlled substance, including marijuana, your offer of employment will be withdrawn.
HSPD\-12 PIV Credential Requirement:
As a national laboratory, PNNL is responsible for adhering to the Homeland Security Presidential Directive 12 (HSPD\-12\) and Department of Energy (DOE) Order 473\.1A, which require new employees to obtain and maintain a HSPD\-12 Personal Identify Verification (PIV) Credential. To obtain this credential, new employees must successfully complete the applicable tier of federal background investigation post hire and receive a favorable federal adjudication. The tier of federal background investigation will be determined by job duties and national security or public trust responsibilities associated with the job. All tiers of investigation include a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last 1 to 7 years (depending on the applicable tier of investigation). Illegal drug activities include marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.
For foreign national candidates:
If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) Federal risk determination to maintain employment. Once you meet the three\-year residency requirement thereafter, you will be required to obtain a PIV credential to maintain employment. The tier of federal background investigation required to obtain the PIV credential will be determined by job duties at the time you become eligible for the PIV credential.
Salary Context
This $100K-$150K range is in the lower quartile for Research Engineer roles in our dataset (median: $202K across 52 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 3,823 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Pacific Northwest National Laboratory, 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 Required
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 $260,000 based on 434 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($125K) sits 52% below the category median. Disclosed range: $100K to $150K.
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.
Pacific Northwest National Laboratory AI Hiring
Pacific Northwest National Laboratory has 1 open AI role right now. They're hiring across Research Engineer. Based in Portland, OR, US. Compensation range: $150K - $150K.
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 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 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).
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 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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