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About This Role
Hybrid
Payroll Title:
RSCH DATA ANL 3 RP
Department:
CLIMATE/ATMOS SCI/PHY OCEANOG
Hiring Pay Scale
$96,075 \- $136,710/ Annually
Worksite:
Hybrid
Appointment Type:
Career
Appointment Percent:
100%
Union:
RP Contract
Total Openings:
1
Work Schedule:
8 hrs/day
\#140859 Machine Learning Scientist
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Filing Deadline: Fri 8/21/2026
UC San Diego values and welcomes people from all backgrounds. If you are interested in being part of our team, possess the needed licensure and certifications, and feel that you have most of the qualifications and/or transferable skills for a job opening, we strongly encourage you to apply.
*UCSD Layoff from Career Appointment*: Apply by 08/12/2026 for consideration with preference for rehire. All layoff applicants should contact their Employment Advisor.
*Reassignment Applicants*: Eligible Reassignment clients should contact their Disability Counselor for assistance.
DESCRIPTION
===============
The CW3E machine learning team is recruiting a scientist to work on the development of artificial intelligence (AI) weather prediction models. The successful candidate will apply their background and expertise in computational science to develop, support, and execute projects of broad scope and complexity that address CW3E's objectives, with a focus on modeling, analyzing, and predicting extreme weather and water events.
The position will contribute directly to ongoing developments at CW3E in the domain of AI weather prediction, including novel architecture design and ensemble strategies. May also develop innovative deep learning\-based post\-processing methods for quantitative precipitation forecasting (QPF) as well as forecasting other relevant variables, e.g., temperature, integrated water vapor transport (IVT) for the benefit of water management.
Communicates research findings through peer\-reviewed journal publications, conference presentations, technical reports, and other publications, as needed. May give technical presentations to associated research and technology groups and management, and represent the organization at national and international meetings, conferences, and committees. Supports proposal development and contributes to ongoing efforts on the strategic growth of computing platforms and improvement of data management procedures.
QUALIFICATIONS
==================
- Bachelor's degree in meteorology, atmospheric sciences, climate science or related field. Master's degree or PhD preferred.
- Experience in implementing machine learning methods for weather/climate research, analysis of dynamical model (e.g., WRF) outputs, and publishing research results.
- Experience in handling artificial intelligence weather models and numerical weather prediction model simulations, and developing methods for deterministic and probabilistic predictions of hydrometeorological variables.
- Knowledge of operational forecasting models and products from NOAA and ECMWF. Knowledge of observational and reanalysis data sets relevant to US West coast meteorology and climate.
- Thorough skills associated with statistical analysis and systems programming. Strong experience in scientific programming, working in a Unix environment, and with scripting languages such as Python, R, or Matlab is highly desirable.
- Experience using common machine learning software (Tensorflow, Keras, PyTorch, Scikit\-Learn, etc.) on cloud computing environments (AWS, Azure, etc.).
- Skills to communicate complex information in a clear and concise manner both verbally and in writing. Skills in scientific writing for peer\-reviewed journal publications, scientific graphical representation, and experience presenting at scientific conferences (oral and poster presentations).
- Thorough skills in analysis and consultation.
- Thorough knowledge of research function. In\-depth experience as an independent researcher.
- Skills in project management. Strong time management skills. Demonstrated ability to prioritize tasks and meet deadlines.
- Research skills at a level to evaluate alternate solutions and develop recommendations. This includes proposing new analyses, new conceptual ideas, or new workflow recommendations.
- Excellent interpersonal skills including thoughtfulness, diplomacy and flexibility with the ability to work independently or within a team framework in conjunction with principles of community with staff, faculty, researchers, and students.
SPECIAL CONDITIONS
======================
- Job offer is contingent upon satisfactory clearance based on Background Check results.
*Pay Transparency Act*
Annual Full Pay Range: Unclassified \- No data available (will be prorated if the appointment percentage is less than 100%)
Hourly Equivalent: Unclassified \- No data available
Factors in determining the appropriate compensation for a role include experience, skills, knowledge, abilities, education, licensure and certifications, and other business and organizational needs. The Hiring Pay Scale referenced in the job posting is the budgeted salary or hourly range that the University reasonably expects to pay for this position. The Annual Full Pay Range may be broader than what the University anticipates to pay for this position, based on internal equity, budget, and collective bargaining agreements (when applicable).
If employed by the University of California, you will be required to comply with our Policy on Vaccination Programs, which may be amended or revised from time to time. Federal, state, or local public health directives may impose additional requirements.
To foster the best possible working and learning environment, UC San Diego strives to cultivate a rich and diverse environment, inclusive and supportive of all students, faculty, staff and visitors. For more information, please visit UC San Diego Principles of Community.
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.
For the University of California’s Anti\-Discrimination Policy, please visit: https://policy.ucop.edu/doc/1001004/Anti\-Discrimination
UC San Diego is a smoke and tobacco free environment. Please visit smokefree.ucsd.edu for more information.
Misconduct Disclosure Requirement: As a condition of employment, the final candidate who accepts an offer of employment will be required to disclose if they have been subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct; or have filed an appeal of a finding of substantiated misconduct with a previous employer.
a. "Misconduct" means any violation of the policies governing employee conduct at the applicant’s previous place of employment, including, but not limited to, violations of policies prohibiting sexual harassment, sexual assault, or other forms of harassment, or discrimination, as defined by the employer. For reference, below are UC’s policies addressing some forms of misconduct:
- UC Sexual Violence and Sexual Harassment Policy
- UC Anti\-Discrimination Policy
- Abusive Conduct in the Workplace
Job Details
---------------
Date Posted
08/07/2026
Salary Context
This $96K-$136K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At UC San Diego, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($116K) sits 46% below the category median. Disclosed range: $96K to $136K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
UC San Diego AI Hiring
UC San Diego has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $136K - $136K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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