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About This Role
Job #JPF11372
- Surgery-General / David Geffen School Of Medicine / UCLA
Position overview
Position title: Postdoctoral Scholar Salary range: The posted UC Salary Scales set the minimum pay determined by rank and step at appointment. See Table 23: Postdoctoral Scholar-Employee, Postdoctoral Scholar-Fellow, Postdoctoral Scholar-Paid Direct, Fiscal Year.
Application Window
Open date: October 13, 2026
Next review date: Monday, Nov 2, 2026 at 11:59pm (Pacific Time)
Apply by this date to ensure full consideration by the committee.
Final date: Thursday, Dec 31, 2026 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.
Position description
Do you want your AI research to move beyond benchmarks and make an impact in real-world healthcare? UCLA’s Center for Advanced Surgical and Interventional Technology (CASIT), part of the Department of Surgery at the David Geffen School of Medicine, is seeking a highly motivated Postdoctoral Scholar with strong expertise in computer vision, multimodal AI, machine learning, and/or agentic AI to help build the next generation of intelligent digital health solutions. You will work side by side with clinicians, engineers, researchers, and industry partners to transform multimodal health data—including wearable and sensor data, medical images and video, speech, human movement, physiological signals, and clinical information—into AI-powered tools for digital biomarkers, remote monitoring, precision health, clinical decision support, and intelligent surgical systems, with the ultimate goal of translating AI innovation into measurable improvements in patient care.
About the Role:
We are looking for a researcher with a Ph.D. in Computer Science, Biomedical Engineering, Electrical and Computer Engineering, Data Science, Robotics, Computational Health, or a closely related field and a strong record in AI, machine learning, digital health, or biomedical data science. You will lead interdisciplinary projects that apply advanced computational methods to clinically meaningful problems across the continuum of care—from early detection and functional assessment to intervention, surgery, recovery, and monitoring at home. Prior clinical experience is not required; scientific curiosity, strong quantitative skills, and a genuine interest in learning from clinicians and solving real-world healthcare problems are essential.
What You Will Work On:
Depending on your strengths and interests, projects may include:
- Digital biomarkers and multimodal health assessment: Develop AI models that integrate wearable sensors, movement, speech, physiological signals, imaging, video, and clinical data to characterize health status, frailty, resilience, cognitive-motor function, and disease progression.
- Wearables and remote monitoring: Transform longitudinal data from wearable and connected technologies into clinically actionable measures for monitoring patients in the hospital, at home, and in the community.
- Computer vision for healthcare: Apply computer vision to medical images, wound images, surgical video, and other visual health data for automated assessment, monitoring, prediction, and decision support.
- Agentic and generative AI for clinical care: Develop systems based on large language and vision-language models that reason across multimodal data, interact with tools, support clinical workflows, automate documentation, and enable personalized monitoring and care coordination.
- AI for brain and functional health: Combine speech, mobility, dual-task performance, wearable sensing, and clinical biomarkers to better characterize brain health, intrinsic capacity, and resilience to age- and disease-related decline.
- Intelligent surgical systems: Develop AI approaches for surgical video understanding, workflow analysis, skill assessment, robotic surgery, human–robot collaboration, and perioperative decision support.
- Digital interventions and precision health: Use data from telehealth, exercise, rehabilitation, and connected health programs to identify treatment response, engagement, and opportunities for personalized intervention.
Key Responsibilities:
- Lead the design, development, and validation of AI and digital health research projects, from defining clinically meaningful questions to developing and evaluating working prototypes.
- Develop machine learning, deep learning, multimodal, computer vision, time-series, and/or foundation-model approaches for biomedical and clinical data.
- Build and curate multimodal datasets and analysis pipelines in compliance with IRB, HIPAA, and institutional requirements.
- Develop and validate digital biomarkers and AI-enabled decision-support tools with attention to reliability, generalizability, safety, and human oversight.
- Publish in leading AI, biomedical engineering, digital health, and clinical venues.
- Contribute to multidisciplinary grant proposals to federal agencies such as NIH, NSF, and ARPA-H, as well as industry-sponsored research.
- Mentor graduate and undergraduate trainees and collaborate closely with clinicians, engineers, researchers, and industry partners.
Qualifications:
Required
- Ph.D. in Computer Science, Biomedical Engineering, Electrical and Computer Engineering, Data Science, Robotics, Computational Health, or a related quantitative field, conferred by the start date.
- Strong research and publication record in AI, machine learning, computer vision, digital health, biomedical engineering, medical AI, or a closely related area.
- Expertise in one or more areas such as deep learning, computer vision, multimodal learning, time-series analysis, signal processing, foundation models, digital biomarkers, or agentic AI.
- Strong programming and data-analysis skills, particularly in Python and modern machine-learning frameworks such as PyTorch or equivalent.
- Experience working with complex, high-dimensional datasets.
- Clear scientific communication skills and a collaborative style suited to multidisciplinary research.
Preferred
- Experience with wearable sensors, digital biomarkers, medical imaging, speech analytics, human movement, remote monitoring, or physiological signals.
- Experience with multimodal or longitudinal data, including fusion of video, sensor, audio, imaging, or clinical information.
- Knowledge of large language models, vision-language models, generative AI, or agentic AI methods such as tool use, planning, retrieval-augmented generation, or multi-agent systems.
- Experience with surgical or medical data, including surgical video, endoscopy, medical imaging, robotics, or perioperative datasets.
- Experience deploying models in real-world or real-time settings, including efficient inference or edge computing.
- Interest in trustworthy AI for healthcare, including uncertainty estimation, explainability, bias, privacy, safety, and human-AI interaction.
- Experience contributing to grant proposals, mentoring students, or translating research toward clinical validation.
The posted UC Salary Scales set the minimum pay determined by rank and step at appointment. See Table 23: Postdoctoral Scholar-Employee, Postdoctoral Scholar-Fellow, Postdoctoral Scholar-Paid Direct, Fiscal Year.
About Us:
CASIT brings clinicians, engineers, data scientists, and researchers together to develop technologies that improve health across the continuum of care. Our work extends from AI-enabled surgical and interventional systems to wearable sensing, digital biomarkers, remote patient monitoring, speech and mobility analytics, wound monitoring, telehealth, healthy aging, and functional resilience. We are particularly interested in combining multimodal data to understand not only disease, but also function, recovery, resilience, and changes in health over time. Across all of our programs, the goal is the same: to move beyond proof-of-concept algorithms and develop technologies that can be clinically validated, deployed in real-world settings, and ultimately improve patient outcomes.
Why Join Us?
- Direct Clinical Environment: Work within UCLA Health and the Department of Surgery, side by side with clinicians, and ground your research in meaningful real-world healthcare problems.
- Broad Digital Health Portfolio: Contribute to projects spanning AI, digital biomarkers, wearables, computer vision, remote monitoring, precision health, and intelligent surgical systems.
- Unique Multimodal Data: Work with combinations of speech, movement, wearable sensors, images, video, physiological signals, and clinical information.
- Translational Impact: Take projects beyond algorithm development toward prototypes, clinical validation, implementation, and real-world deployment.
- Mentorship and Leadership: Work under the direction of Dr. Bijan Najafi, Professor of Surgery and Research Director of CASIT, with opportunities to lead projects, mentor trainees, and develop an independent research program.
- Career Development: Build a strong profile for academic or industry careers through hands-on experience in scientific leadership, grant writing, multidisciplinary collaboration, and clinical translation.
Joining our team offers a rare combination of cutting-edge AI research, digital health innovation, direct clinical exposure, and real-world translational impact.
The shared values of the DGSOM are expressed in the Cultural North Star, which was developed by members of our community and affirms our unswerving commitment to doing what’s right, making things better, and being kind. These are standards to which we hold ourselves, and one another. Please read more about this important DGSOM program.
Interested candidates can apply through the UC Recruit System.
For additional information please contact:
Bijan Najafi, Ph.D.
Professor of Surgery & BioEngineering
Research Director, Center for Advanced Surgical & Interventional Technology (CASIT)
Qualifications
Basic qualifications Ph.D. (or equivalent) in Computer Science, Biomedical Engineering, Electrical and Computer Engineering, Data Science, Robotics, Computational Health, or a related quantitative field
Application Requirements
Document requirements
- Curriculum Vitae - Your most recently updated C.V. with a full publication list
- Cover Letter - 1 to 2 page cover letter describing your research experience and how your expertise could advance AI and digital health innovation.
- Reprint of Publications - 2 to 3 reprints of repesentative publications
- Optional - Links to code repositories, software, project websites, or demos are encouraged
(Optional)
- UCLA Mission Statement - As the nation's premier public research university, UC's mission is the creation, dissemination, preservation and application of knowledge for the betterment of our global society. We have a particular responsibility to the people of California which we express in the excellence of the education we provide, the impact of the research we do, the comprehensive, life-saving medical services we provide, and the public service mission we are devoted to. The University of California promotes the social mobility of its students, equips them with the tools and experience that furthers their ambitions, and regards their accomplishments across the life span as evidence of the profoundly positive impact of higher education.
The UCLA campus has expressed these goals in its strategic plan as follows:
Deepen our engagement with Los Angeles
Expand our reach as a global university
Enhance our research and creative activities
Elevate how we teach
Become a more effective institution
Prompt for candidates for recruitment:
Reflecting on your personal and professional experiences, highlight your past contributions and future commitments to advancing UCLA's mission as embodied in the 2023-28 strategic plan. These accomplishments and ambitions may be discussed in the context of describing your teaching, scholarship, and service Reference requirements
- 3 required (contact information only)
Contact information for references is being requested but will not be used at this time. Final candidates will be
notified in advance if the need to contact references is used as part of the final selection process.
Apply link: https://recruit.apo.ucla.edu/JPF11372 Help contact: [email protected]
About UCLA
As a University employee, you will be required to comply with all applicable University policies and/or collective bargaining agreements, as may be amended from time to time. Federal, state, or local government directives may impose additional requirements.
The University of California is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected status under state or federal law.
As a condition of employment, the finalist will be required to disclose if they are subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct.
- “Misconduct” means any violation of the policies or laws governing conduct at the applicant’s previous place of employment, including, but not limited to, violations of policies or laws prohibiting sexual harassment, sexual assault, or other forms of harassment, discrimination, dishonesty, or unethical conduct, as defined by the employer.
- UC Sexual Violence and Sexual Harassment Policy
- UC Anti-Discrimination Policy for Employees, Students and Third Parties
- APM - 035: Affirmative Action and Nondiscrimination in Employment
Job location
Los Angeles, CA
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 University of California Los Angeles, 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. Mid-level AI roles across all categories have a median of $193,200.
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.
University of California Los Angeles AI Hiring
University of California Los Angeles has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US.
Location Context
AI roles in Los Angeles pay a median of $210,000 across 1,486 tracked positions.
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
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