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About This Role
Are you passionate about advancing healthcare through innovative methods and cutting\-edge technology? Do you have experience working across stakeholder groups to build and implement health AI solutions that directly impact clinical care? If so, Stanford University invites you to join GUIDE\-AI within the Computational Medicine division as a Research Engineer to shape how AI is developed, evaluated, and deployed in healthcare.
GUIDE\-AI (Guidance for the Use, Implementation, Development, and Evaluation of AI) is a Stanford Medicine initiative that spans Stanford University and Technology and Digital Solutions at Stanford Health Care. The key tenets under GUIDE\-AI's mission are:
- Building and deploying healthcare AI solutions to address key opportunities to advance clinical care.
- Establishing evaluation and monitoring methods to assess deployed AI tools at Stanford Health Care and beyond.
- Disseminating actionable learnings to support high\-value AI\-augmented care for all.
As a Research Engineer for GUIDE\-AI, you will lead technical development across a portfolio of healthcare AI projects, owning everything from data pipeline design to model evaluation and deployment. You will manage and/or work with technical teams across Stanford University, Stanford Health Care, and external partners to create scalable, collaboratively developed methods and tools. Potential projects may include developing innovative monitoring and evaluation methods, patient eligibility\-matching tools, and generative and agentic systems to streamline clinical care delivery.
Beyond development, you will contribute to statistical analyses, prospective AI evaluation designs, and grant proposals, shaping study methodologies and supporting funding applications to advance GUIDE\-AI’s mission. Additionally, you will play a key role in mentoring trainees and students, maintaining well\-documented code repositories, and driving long\-term strategic initiatives for AI evaluation in healthcare.
Duties include:
- Conceptualize design, implement, and develop solutions for complex system/programs independently, such as developing and implement Python and SQL\-based tools for AI evaluation.
- Engage in long\-term strategic planning in collaboration with staff and project leadership.
- Work with a variety of users to gain information, and develop intra\-system tradeoffs between different users, as necessary; interact with a diverse client base and outside vendor contacts.
- Document system builds and application configurations; maintain and update documentation as needed.
- Provide technical analysis, design, development, conversion, and implementation work.
- Work as a project leader, as needed, for projects of moderate complexity.
- Serve as a technical resource for grant applications.
- Contribute to statistical analyses, prospective AI evaluation designs, and grant proposals, shaping study methodologies, constructing statistical plans, and supporting funding applications to advance GUIDE\-AI’s mission
- Compare, evaluate, and implement new features and technologies, and integrate them into the computing environment.
- Follow team software development methodology.
- Mentor lower\-level software developers, including trainees and students
- Actively collaborate with interdisciplinary groups to advance projects that drive both healthcare delivery and technical innovation.
DESIRED QUALIFICATIONS:
- Proficiency in Python and SQL, with hands\-on experience in data analysis, statistical modeling, and AI evaluation.
- Expertise in developing and implementing algorithms (e.g., neural networks, clustering, embedding models).
- Expertise in full\-cycle application development, including design, testing, deployment, and optimization.
- Strong analytical and problem\-solving skills, with the ability to define and address complex technical challenges.
- Effective communicator, able to collaborate with both technical and non\-technical stakeholders.
- Proven ability to lead structured team development projects, ensuring efficient workflows and high\-quality outcomes.
- Solid foundation in probability and statistics, including power analysis, hypothesis testing, and uncertainty quantification (e.g., constructing confidence intervals, bootstrapping).
- Experience in AI model evaluation, including performance metric selection, fairness and bias assessment, and data visualization.
- (Preferred) Direct collaboration with clinicians, evidenced by first\-author publications in medical journals and/or co\-development of clinically\-facing tools.
- (Preferred) Experience with real\-world clinical data models and standards (e.g., FHIR, OMOP) is strongly preferred.
- (Preferred) Experience monitoring deployed AI tools, including performance drift detection, alerting, and iterative model updating.
- (Preferred) Experience with prospective AI evaluations, including clinical trial design, real\-world validation, and sample size estimation.
EDUCATION \& EXPERIENCE (REQUIRED):
- Bachelor's degree and five years of relevant experience, or a combination of education and relevant experience.
KNOWLEDGE, SKILLS AND ABILITIES (REQUIRED):
- Expertise in designing, developing, testing, and deploying applications.
- Proficiency with application design and data modeling.
- Ability to define and solve logical problems for highly technical applications.
- Effective communication skills with both technical and non\-technical clients.
- Ability to lead activities on structured team development projects.
- Ability to select, adapt, and effectively use a variety of programming methods.
- Knowledge of application domain.
PHYSICAL REQUIREMENTS:
- Constantly perform desk\-based computer tasks.
- Frequently sit, grasp lightly/fine manipulation.
- Occasionally stand/walk, writing by hand.
- Rarely use a telephone, lift/carry/push/pull objects that weigh up to 10 pounds.
WORKING CONDITIONS:
- May work extended hours, evening and weekends.
- Travel on campus to school/units
WORKING STANDARDS:
- Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.
- Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for safety; communicates safety concerns; uses and promotes safe behaviors based on training and lessons learned.
- Subject to and expected to comply with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in the University’s Administrative Guide, http://adminguide.stanford.edu/.
The expected pay range for this position is $138,402 \- $163,985 per annum.
Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs.
At Stanford University, base pay represents only one aspect of the comprehensive rewards package. The Cardinal at Work website (https://cardinalatwork.stanford.edu/benefits\-rewards) provides detailed information on Stanford’s extensive range of benefits and rewards offered to employees. Specifics about the rewards package for this position may be discussed during the hiring process.
Why Stanford is for You
Imagine a world without search engines or social platforms. Consider lives saved through first\-ever organ transplants and research to cure illnesses. Stanford University has revolutionized the way we live and enrich the world. Supporting this mission is our diverse and dedicated 17,000 staff. We seek talent driven to impact the future of our legacy. Our culture and unique perks empower you with:
- Freedom to grow. We offer career development programs, tuition reimbursement, or audit a course. Join a TedTalk, film screening, or listen to a renowned author or global leader speak.
- A caring culture. We provide superb retirement plans, generous time\-off, and family care resources.
- A healthier you. Climb our rock wall, or choose from hundreds of health or fitness classes at our world\-class exercise facilities. We also provide excellent health care benefits.
- Discovery and fun. Stroll through historic sculptures, trails, and museums.
- Enviable resources. Enjoy free commuter programs, ridesharing incentives, discounts and more.
*The job duties listed are typical examples of work performed by positions in this job classification and are not designed to contain or be interpreted as a comprehensive inventory of all duties, tasks, and responsibilities. Specific duties and responsibilities may vary depending on department or program needs without changing the general nature and scope of the job or level of responsibility. Employees may also perform other duties as assigned.*
*Consistent with its obligations under the law, the University will provide reasonable accommodations to applicants and employees with disabilities. Applicants requiring a reasonable accommodation for any part of the application or hiring process should contact Stanford University Human Resources submitting a* *contact form**.*
*Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.*
Salary Context
This $138K-$163K range is below the median for Research Engineer roles in our dataset (median: $185K across 51 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 3,824 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Stanford 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 $260,000 based on 401 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($151K) sits 42% below the category median. Disclosed range: $138K to $163K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Stanford University AI Hiring
Stanford University has 1 open AI role right now. They're hiring across Research Engineer. Based in Stanford, CA, US. Compensation range: $163K - $163K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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