Interested in this AI/ML Engineer role at Stanford Health Care?
Apply Now →Skills & Technologies
About This Role
1.0 FTE Full time Day - 08 Hour R2660734 148610003 SBC Business Intelligence Allied Health PALO ALTO, 3373 Hillview Ave, California If you're ready to be part of our legacy of hope and innovation, we encourage you to take the first step and explore our current job openings. Your best is waiting to be discovered.
Day - 08 Hour (United States of America)This is a Stanford Blood Center job.
A Brief Overview
The SBC Sr Data & AI Solutions Engineer designs Artificial Intelligence (AI), predictive algorithms and other emerging machine learning (ML) innovations and integrates into SBC operations to improve outcomes, drive resource optimization and help meet blood transfusion and transplantation requirements. This role also leads end-to-end Extract Transformations and Load (ETL) pipelines to unify data across databases, data marts, and the EDW; enhances BI capabilities to drive data-driven decisions, optimize blood production and testing workflows, and monitor performance with proactive support.
Locations
Stanford Blood Center
What you will do* Applies solid software development and object-oriented programming (OOP) skills to build applications and front-end web solutions.
- Builds, deploys, and maintains AI and ML solutions across clinical, laboratory, and operational domains to improve outcomes while upholding safety, ethical governance and cost-effectiveness
- Utilizes Python data science capabilities and leverages libraries (virtual environments, REST APIs) to accelerate analytics delivery, solve complex problems efficiently, and build scalable data pipelines and applications, ensuring reliable technical support and timely troubleshooting.
- Leverages deep expertise in large language models (LLMs) and smaller language models (SLMs) to fine-tune, engineer prompts, and deploy AI solutions that enhance business analytics, product development, data-driven decision making to help boost productivity.
- Develops/enhances BI and ETL processes that reflect best practices for error handling, operations logging and alerting, scheduling, and job control. Prepares specifications, designs, data models and diagrams from which databases can be developed and manages a common data model to harmonize relational databases, tune queries to optimize the EDW and BI architecture while monitoring performance with proactive support.
- Applies advanced data analysis, forecasting, predictions and simulation methods to project demand for services and blood products, driving supply chain optimization and optimal resource allocation.
- Identifies improvements to operational processes including productivity analysis, process engineering and optimization and leads projects to implement such improvements applying solid project management skills, team/user communications, etc.
- Assumes broader responsibilities to meet evolving business needs and uphold department efficiency, while mentoring BI analysts to grow their capabilities
- Complies with governmental regulations and Stanford Health Care and Blood Center policies regarding health and safety. Observes and supports good health and safety practices.
- Strictly observes privacy and security related policies, procedures and practices to preserve the integrity and confidentiality of medical and other sensitive information pertaining to donors, patients, research subjects, and team members. Acts as a responsible information steward and treat information as sensitive and confidential in accordance with federal and state laws and with professional ethics, accreditation standards and legal requirements. Do not disclose protected health information inappropriately.
- May be required to enter areas where other individuals work with human blood; potential may exist for unanticipated exposure to bloodborne pathogens by splash or spill.
Education Qualifications* Bachelor's Degree in engineering, computer science, data science or related field plus 4 years of experience with applied problems required
- Master's Degree in engineering, computer science, data science or related field preferred
Experience Qualifications* 3+ years to 5 years hands-on experience designing and developing software (across the full lifecycle), AI, and BI solutions; accountable for a complex EDW environment, driving performance with robust ETL processes required
Required Knowledge, Skills and Abilities* Experience developing, evaluating, and implementing Machine Learning and Artificial Intelligence models while partnering with individuals across clinical and operational areas to deploy algorithms and solutions that can lead to better outcomes and optimization of resources while validating both researcher-built and vendor-provided algorithms or AI solutions
- Advanced OOP-based architecture and optimization for software and systems; proficient in Python (Django) and Java; HTML and CSS skills; experience with AI/ML deployment, including large language models (LLMs); proven performance tuning ability.
- End-to-end BI lifecycle ownership, including design, development, and deployment of BI solutions; BI tool administration; ETL design and development; EDW architecture; and delivery of robust reporting and analytics
- Extensive experience leveraging advanced analytics, forecasting, predictive modeling, and simulation to optimize operations; proven ability to communicate results to diverse stakeholders and management to drive engagement.
- Proficiency with BI and data visualization tools (i.e., Power BI, Tableau, SSRS, SAP BusinessObjects); SQL expertise in Oracle and MS SQL Server (Management Studio); SSIS; Azure DevOps Server; applying patches/upgrades; and monitoring hardware utilization for peak performance.
- Applies Lean principles to BI workflows, problem-solving, and Gemba-style observation rounds to identify waste and improve process flow, simulation for analyzing production systems and optimal production scenarios.
- Seeks and embraces coaching and mentoring and effectively formulates. Exercises sound judgement in prioritizing work independently, balancing speed and accuracy while maintaining quality and reliable outputs with limited problems or defects.
Physical Demands and Work Conditions
Physical Demands* Constant Sitting.
- Occasional Walking.
- Occasional Standing.
- Seldom Bending.
- Never Squatting.
- Never Climbing.
- Seldom Kneeling.
- Never Crawling.
- Constant Hand Use.
- Frequent Repetitive Motion Hand Use.
- Frequent Grasping.
- Occasional Fine Manipulation.
- Occasional Pushing and Pulling.
- Seldom Reaching (above shoulder level).
- Occasional Twisting and Turning (Neck and Waist).
- Frequent Vision (Color, Peripheral, Distance, Focus).
- Constant Other (please list each item under Comments). Computer use
Lifting* Seldom lifting of 0 - 10 lbs.
- Seldom lifting of 11 - 20 lbs.
- lifting of 21 - 30 lbs. 0 to 0 in height
- lifting of 31 - 40 lbs. 0 to 0 in height
- lifting of 40+ lbs. 0 to 0 in height
Carrying* Seldom lifting of 0 - 10 lbs.
- Seldom lifting of 11 - 20 lbs.
- lifting of 21 - 30 lbs. 0 to 0 in height
- lifting of 31 - 40 lbs. 0 to 0 in height
- lifting of 40+ lbs. 0 to 0 in height
Blood Borne Pathogens* Category III - Tasks that involve NO exposure to blood, body fluids or tissues, and Category I tasks that are not a condition of employment
These principles apply to ALL employees:
SHC Commitment to Providing an Exceptional Patient & Family Experience
Stanford Health Care sets a high standard for delivering value and an exceptional experience for our patients and families. Candidates for employment and existing employees must adopt and execute C-I-CARE standards for all of patients, families and towards each other. C-I-CARE is the foundation of Stanford’s patient-experience and represents a framework for patient-centered interactions. Simply put, we do what it takes to enable and empower patients and families to focus on health, healing and recovery.
You will do this by executing against our three experience pillars, from the patient and family’s perspective:* Know Me: Anticipate my needs and status to deliver effective care
- Show Me the Way: Guide and prompt my actions to arrive at better outcomes and better health
- Coordinate for Me: Own the complexity of my care through coordination
Equal Opportunity Employer Stanford Health Care (SHC) strongly values diversity and is committed to equal opportunity and non-discrimination inall ofits policies and practices, including the area of employment. Accordingly, SHC does not discriminate against any person on the basis of race, color, sex, sexual orientation or gender identity and/or expression, religion, age, national or ethnic origin, political beliefs, marital status, medical condition, genetic information, veteran status, or disability, or the perception of any of the above. People of all genders, members of all racial and ethnic groups, people with disabilities, and veterans are encouraged to apply. Qualified applicants with criminal convictions will be considered after an individualized assessment of the conviction and the job requirements.
Base Pay Scale: Generally starting at $72.64 - $96.23 per hour The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to, internal equity, experience, education, specialty and training. This pay scale is not a promise of a particular wage.
Salary Context
This $149K-$199K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Stanford Health Care, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($174K) sits 18% below the category median. Disclosed range: $149K to $199K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Stanford Health Care AI Hiring
Stanford Health Care has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Palo Alto, CA, US. Compensation range: $199K - $199K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.