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About This Role
Stanford University is seeking a Machine Learning Engineer to perform advanced technical research for the ARPA\-H/BDF grant. The grant required use of AI and ML tools for modeling and building biomedical applications that will be evaluated by clinicians. The aim is to understand the performance, safety, effectiveness, reliability, and transparency of ML/AI models intended for real\-world deployment.
Reporting to the technical manager of the grant, and with guidance and dotted line reporting to senior faculty leaders, the individual will build end\-to\-end data pipelines and infrastructure for ML models used in the grant. They will build robust and modular software engineering infrastructures for training and inference of ML models that can be used for a variety of downstream applications and will use their knowledge to make recommendations and design decisions for languages, tools, and platforms used in software and data projects.
About Us:
The Department of Biomedical Data Science merges the disciplines of biomedical informatics, biostatistics, computer science and advances in AI. The intersection of these disciplines is applied to precision health, leveraging data across the entire medical spectrum, including molecular, tissue, medical imaging, EHR, biosensory and population data.
You Will Find This Position a Good Fit If:
- You are passionate about transforming raw healthcare data into valuable insights.
- You believe in the critical role of AI in advancing machine learning in healthcare.
- You thrive in environments where you can work independently on complex data challenges while collaborating with multidisciplinary teams.
- You are excited to work with patient\-level data and embrace challenges related to data diversity and complexity.
Duties include:
- Support complex scientific and research programs related to area of specialization; analyze data, monitor and oversee experimental process, and design and develop prototypes, specialized equipment, and/or systems.
- Collaborate with scientists, engineers, or senior administrative officers to oversee complex non\-routine analyses, select optimum solutions, and perform corrective modifications to equipment and system designs.
- Carry out all activities, including troubleshooting and resolving routine problems for scientist or engineers, independently.
- Collaborate with senior engineers and scientists to design and develop special purpose equipment and/or systems.
- Participate in the planning, design, and implementation of scientific or engineering initiatives, and work toward project
- objective.
- Oversee a laboratory space or unit, and supervise the work of technicians and other staff associated with the group.
- Serve as a resource in review of research proposals and research capabilities, and make recommendations.
- Establish, communicate, and enforce compliance with health and safety policies and procedures.
- Develop training manuals and safety guidelines, and train new instrumentation users, researchers, and/or technical staff.
- Perform supervisory duties, including overseeing the work of technicians and other staff associated with the group/project, supervising the regular installation, maintenance, and operation of complex scientific or engineering projects, and training technicians, operators, and others working in particular scientific or engineering function area.
*\* \- Other duties may also be assigned*
*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.*
The expected pay range for this position is $122,929 to $145,389 per annum.
Stanford University provides pay ranges representing its good faith estimate of the salary or hourly wage the university reasonably expects to pay for a position upon hire. 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.
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 at stanfordelr@stanford.edu . For all other inquiries, please submit 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. Stanford welcomes applications from all who would bring additional dimensions to the University's research mission.*
Qualifications:
DESIRED QUALIFICATIONS:
- Ability to install, configure, and implement machine learning algorithms in modern training platforms (such as PyTorch, JAX) and inference platforms (such as Hugging Face, gradio, streamlit
- Experience with cloud infrastructure and CI/CD
- Experience overseeing, developing or implementing machine learning operations (MLOps) processes. Experience working with healthcare data.
- Experience in building software and data infrastructure for analytics team, including ability to write Python and BigQuery SQL for processing large datasets
- Research and prototype state\-of\-the\-art foundation models for multi\-modal data including radiology, EMR and pathology
- Design algorithm evaluation frameworks, benchmark datasets and report metrics
- Experience in shared code environments such as GitHub and collaborate with other developers and be responsive to GitHub issues and pull requests.
- Lead code reviews for projects/systems as an independent reviewer applying design principles, coding standards and best practices
- Experience working in a HIPAA regulated environment
- Experience with publications in AI for medical applications in healthcare journals or ML conferences a plus
PREFERRED QUALIFICATIONS:
Proficiency with containerization tools (e.g., Docker).
Familiarity with healthcare data standards and regulatory requirements.
EDUCATION \& EXPERIENCE (REQUIRED):
Bachelor’s degree in engineering, science, or related field and three years of relevant experience; or a combination of education and relevant experience.
KNOWLEDGE, SKILLS AND ABILITIES (REQUIRED):
- Demonstrated knowledge and skills of advanced scientific or engineering principles and practices.
- Demonstrated experience applying complex scientific and engineering principles and performing special technical services involving both development and performance.
- In\-depth experience with software applications, systems, or programs relevant for the job.
- Ability to independently oversee and manage instrumentation or system installation.
- Ability to collaborate with senior engineering and scientific staff to design and develop special purpose equipment and/or systems.
- Experience overseeing the plan, design, and implementation of major scientific or engineering initiatives and ensuring project objective are met.
- Demonstrated ability to review research proposals, evaluate research capabilities, and make recommendations.
- Demonstrated ability to establish, communicate, and enforce compliance with health and safety policies and procedures.
- Experience overseeing a laboratory space or unit and supervising the work of technicians and other staff associated with the group.
- Demonstrated ability to effectively supervise and train a diverse work staff.
CERTIFICATIONS \& LICENSES:
None
PHYSICAL REQUIREMENTS\*:
- Frequently grasp lightly/fine manipulation, perform desk\-based computer tasks, lift/carry/push/pull objects that weigh up to 10 pounds.
- Occasionally stand/walk, sit, twist/bend/stoop/squat, grasp forcefully.
- Rarely kneel/crawl, climb (ladders, scaffolds, or other), reach/work above shoulders, use a telephone, writing by hand, sort/file paperwork or parts, operate foot and/or hand controls, lift/carry/push/pull objects that weigh \>40 pounds.
*\* \- Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of his or her job.*
WORKING CONDITIONS:
- May be exposed to high voltage electricity, radiation or electromagnetic fields, lasers, noise \> 80dB TWA, Allergens/Biohazards/Chemicals /Asbestos, confined spaces, working at heights 10 feet, temperature extremes, heavy metals, unusual work hours or routine overtime and/or inclement weather.
- May require travel.
Salary Context
This $122K-$145K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Stanford University, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($134K) sits 39% below the category median. Disclosed range: $122K to $145K.
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 Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Stanford University AI Hiring
Stanford University has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Redwood City, CA, US, Stanford, CA, US. Compensation range: $145K - $194K.
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 AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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