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About This Role
Spectrum Lab is an interdisciplinary MSU research center established in 1999 and reports to the Vice\-President of Research and Economic Development (VPRED). It is dedicated to innovation in applied optics and photonics with a three\-fold mission to:
- Develop and help commercialize Montana grown opto\-electronic technologies.
- Transfer and develop technologies to Montana companies.
- Provide enhanced educational, research, and employment opportunities for Montana undergraduate and graduate students.
Spectrum Lab’s location close to the MSU\-Bozeman campus and in close proximity to spin\-off companies and other local industry facilitates collaboration with MSU faculty and researchers and our local industry partners. The lab has several research grants and contracts in the areas of remote sensing, coherent imaging, and optical signal processing with average annual expenditures of roughly $1\.5M.
Duties and Responsibilities
- Conduct, report, and publish (when applicable) scientific/engineering research projects in applied optics, photonics and quantum technologies, including conducting independent research, conducting research as a team member, and leading a team of researchers.
- Supervise teams of undergraduate and graduate student researchers on research projects, including assigning tasks, providing technical assistance and advice, and following up on task completion and reporting.
- Depending on the positions title, individual experience and qualification, collaborate with research teams to develop new areas of applied optics, quantum optics and photonics research and actively contribute to obtaining funding through successful research proposals as a key contributor on the proposed work.
- Interact productively with research team, with MSU faculty, or with local companies with the goal of developing and/or helping to commercialize new technologies.
- Execute assignments and tasks that are specialized and advanced in nature with minimum supervision.
Required Qualifications – Experience, Education, Knowledge \& Skills
Research Associate:
- Bachelor’s degree in Optics, Physics, Electrical Engineering, or related field with documentation of 2 or more years of research/laboratory experience in applied optics or quantum technology either in an academic institution or industry OR Master’s degree in Optics, Physics, Electrical Engineering, or related field with documentation of research experience (academic or industrial) in photonics applications or quantum technology.
- Successful record of research or professional experience in the areas of Experimental Microwave Photonics, Optomechanics, Quantum Communications and Quantum networks, Nonlinear optics or Fiber and Free\-space Optics, rare\-earth doped solids, Optical signal processing.
Research Scientist/Engineer:
- Master’s degree in Optics, Physics, Electrical Engineering, or related field with documentation of 3 or more years of experience (academic or industrial) in experimental or applied optics and original work in experimental or applied photonics, and a record of leadership in the execution and conduct of externally (e.g., government) funded research programs in optics, OR Ph.D. degree (including recently awarded) in Optics, Physics, Electrical Engineering, or related field with documentation of original work in experimental or applied optics and demonstrated potential for leadership in the acquisition and conduct of externally (e.g., government) funded research programs in optics.
- Successful record of research or professional experience in the areas of Experimental Quantum Optics, Quantum Communications and Quantum Networks, Nonlinear Optics, Fiber and Free\-space Optics, rare\-earth doped solids, and Optical Signal Processing.
Preferred Qualifications – Experience, Education, Knowledge \& Skills
- Record of scientific publication in peer\-reviewed journals, patents, or presentations at technical conferences.
- Have demonstrated the ability to actively and significantly contributed to the development of an innovative and externally funded research or development program.
- Record of successfully completing and documenting research or development projects.
- Demonstrated business development skills in research and development.
- Good theoretical skills as demonstrated in academic work, technical writing, or work products.
- Demonstrated experience in the use of technical and experimental computer software (such as MATLAB, Python, Mathematica, LabView).
- Demonstrated experience in conducting hands\-on experimental research, including skills in electronics and optics.
- Demonstrated ability in successful technical project management.
The Successful Candidate Will
- Work productively in a professional manner, both autonomously and as a member of a team, prioritizing work in a challenging environment with many demands.
- Interpersonal skills to effectively collaborate with colleagues in academia and private companies.
- Effective written and oral communication skills, publishing results of research in conference proceedings or possess the ability to handle and control material of a sensitive nature.
- Actively propose and significantly contribute to the development of innovative and externally funded research projects.
- Leadership potential and capabilities.
- Ability and desire to significantly contribute to the research missions of Spectrum Lab and the objectives of the grants and contracts.
- Have demonstrated ability to maintain effective attention to detail, to meet deadlines, and to well document and maintain thorough records of their work.
- Ability and desire to work with and engage a wide range of individuals on campus and in the larger research community, including faculty, research staff, students, and colleagues in academia and private companies.
Position Special Requirements/Additional Information
This position includes working in a facility with controlled research restrictions; therefore, applicants have to be U.S. citizens to be eligible for the positions. The positions involve working with, handling, and controlling sensitive information.
Continuation of employment is contingent upon availability of funding.
This job description should not be construed as an exhaustive statement of duties, responsibilities or requirements, but a general description of the job. Nothing contained herein restricts Montana State University’s rights to assign or reassign duties and responsibilities to this job at any time.
Physical Demands
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily with or without reasonable accommodations. The requirements listed above are representative of the knowledge, skill, and/or ability required.
This position has supervisory duties? Yes
Posting Detail Information
Number of Vacancies Multiple Positions
Desired Start Date Upon completion of a successful search
Position End Date (if temporary)
Open Date
Close Date
Applications will be:
Screening of applications will begin on August 5, 2026; however, applications will continue to be accepted until an adequate applicant pool has been established.
Special Instructions
This position includes working in a facility with controlled research restrictions; therefore, applicants have to be U.S. citizens to be eligible for the positions. The position involves working with, handling, and controlling sensitive information.
EEO Statement
Montana State University is an equal opportunity employer. MSU does not discriminate against any applicant on the basis of race, color, religion, creed, political ideas, sex, sexual orientation, gender identity or expression, age, marital status, national origin, physical or mental disability, or any other protected class status in violation of any applicable law.
In compliance with the Montana Veteran’s Employment Preference Act, MSU provides preference in employment to veterans, disabled veterans, and certain eligible relatives of veterans. To claim veteran’s preference, please complete the veteran’s preference information located in the Demographics section of your profile.
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 3,708 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Montana 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 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 $280,000 based on 147 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $120,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and AI Architect ($254,798). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Montana State University AI Hiring
Montana State University has 1 open AI role right now. They're hiring across Research Engineer. Based in Bozeman, MT, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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