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Golden, Colorado, United States
Onsite
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Join the National Laboratory of the Rockies (NLR), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR stands out in the research community for its commitment to an affordable and secure energy future. Spanning foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions. Our work helps strengthen U.S. industries, support job creation, and promote national economic growth.
At NLR, you'll find a mission-driven environment supported by state-of-the-art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being.
The National Laboratory of the Rockies (NLR) is seeking an accomplished full-time software engineer with experience in AI, optimization, applied mathematics, and high-performance computing within the Computational Science Center (CSC) with a focus on advancing energy innovation by supporting world class research and leadership in computational science.
In the CSC, we integrate discovery science, engineering, and mission delivery to translate breakthrough research into operational capabilities, with core strengths in high‑performance computing, AI/ML, modeling and simulation, and visualization. We steward state‑of‑the‑art supercomputing and data facilities, secure research environments (including classified spaces), and specialized testbeds that enable end‑to‑end development and red‑team evaluation. NLR collaborates across DOE program offices and with the defense and intelligence communities, other federal agencies, universities, and industry through public–private partnerships and technology transition pathways, while fostering a culture of scientific excellence, operational rigor, and responsible research.
In this position you will design, develop, and rigorously test software applications and components, utilizing best practices and cutting-edge technologies to pave the way for innovative, collaborative, and hybrid computing solutions supporting NLR’s mission critical research projects.
Candidates will work across the full research-to-implementation stack including designing and evaluating AI models, translating findings into production-ready systems, and occasionally diving into the quantitative underpinnings that make intelligent systems behave reliably in constrained, real-world settings. This isn't a role where AI and optimization live in separate silos. Candidates are expected to reason across both.
Strong software engineering fundamentals are the foundation. Beyond that, candidates should have meaningful experience with modern AI/ML such as deep learning, probabilistic modeling, large-scale inference, foundation models, and applied AI in a specific domain. Candidates understand the difference between a benchmark result and a deployed system, and know how to close that gap.
On the optimization side, candidates are expected to be comfortable with formulating and reasoning about constrained optimization problems. Candidates should possess a willingness to go deep when the work demands it.
Candidates should have a strong technical background, good work ethic, experience in leadership and mentorship, and be comfortable to quickly learn and apply new technologies, as the role will include self-starting and performing technical tasks independently. The role also requires collaboration and effective communication with peers, mentors, and team members. The successful candidate will not shy away from tackling challenges, but rather, demonstrate eagerness to engineer creative solutions. In this vein, the candidate should have a high degree of curiosity, excitement to work on a team, and ability to adapt to needs of different projects and challenges by gaining and utilizing new skills in support of research goals.
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* Must meet educational requirements prior to employment start date.
First-author publication(s)
Open-source software release(s)
Ability to obtain a clearance
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The anticipated closing window for application submission is up to 30 days and may be extended as needed.
NLR takes into consideration a candidate’s education, training, and experience, expected quality and quantity of work, required travel (if any), external market and internal value, including seniority and merit systems, and internal pay alignment when determining the salary level for potential new employees. In compliance with the Colorado Equal Pay for Equal Work Act, a potential new employee’s salary history will not be used in compensation decisions.
* Based on eligibility rules
NLR is committed to maintaining a drug-free workplace in accordance with the federal Drug-Free Workplace Act and complies with federal laws prohibiting the possession and use of illegal drugs. Under federal law, marijuana remains an illegal drug.
If you are offered employment at NLR, you must pass a pre-employment drug test prior to commencing employment. Unless prohibited by state or local law, the pre-employment drug test will include marijuana. If you test positive on the pre-employment drug test, your offer of employment may be withdrawn.
Please note that in order to be considered an applicant for any position at NLR you must submit an application form for each position for which you believe you are qualified. Applications are not kept on file for future positions. Please include a cover letter and resume with each position application.
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All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, domestic partner status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.
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