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About This Role
Department: Neurosurgery - ARIA Project 100 College Street, New Haven, Connecticut Job ID: 138764WD $68,000.00 - $120,500.00 Description
Working at Yale means contributing to a better tomorrow. Whether you are a current resident of our New Haven-based community, eligible for opportunities through the New Haven Hiring Initiative, or a newcomer, interested in exploring all that Yale has to offer, your talents and contributions are welcome. Discover your opportunities at Yale!
Overview
The Department of Neurosurgery is seeking an experienced AI/ML professional and a knowledgeable AI enthusiast to help the ARIA team develop, use, and evaluate AI and apply it to deliver breakthrough research and applications at an unprecedented speed and scale. Guides projects within the research core, supporting core technologies, managing experimental design and data analysis. The Yale School of Medicine Department of Neurosurgery is seeking an AI researcher to develop machine-learning methods for large-scale human brain data within the Aligning Research to Impact Autism (ARIA) program. The work will focus on neural decoding, forecasting, representation learning, and multimodal modeling using intracranial EEG (iEEG/ECoG), scalp EEG, fNIRS, fMRI, and related data. The successful candidate will be an individual who enjoys working both independently and cooperatively with others, from a broad set of disciplinary backgrounds, on multiple projects involving a wide variety of topics and analytical tools.
Job Duties:
Develop, train, and evaluate machine-learning models for multimodal human brain data.
Build models for neural representation learning, forecasting, decoding, and cross-modal analysis.
Contribute to the development of scalable brain foundation models across subjects, recording modalities, and tasks.
Work with large datasets and optimize training pipelines for GPU and HPC environments.
Collaborate with clinicians, neuroscientists, engineers, and other researchers on study design and analysis.
Maintain clear, reproducible code and documentation for model development and evaluation.
Contribute to manuscripts, conference presentations, and grant-related research activities.
Provide technical guidance to students and research staff when appropriate.
Required Skills and Abilities
- Proven experience developing machine-learning or deep-learning models. Proven experience with distributed or multi-GPU training and memory-efficient model training.
- Familiarity with tools such as Slurm, CUDA, Docker or Apptainer, Conda, and scientific computing clusters.
- Strong Python programming skills and practical experience with PyTorch or a comparable deep-learning framework.
- Proven experience with modern neural-network architectures such as Transformers, self-supervised learning, representation learning, generative models, or foundation models.
- Proven experience training models on GPUs and working in Linux and high-performance computing environments.
Preferred Skills and Abilities
- Proven experience with neural or physiological data, including iEEG/ECoG, EEG, fNIRS, fMRI, MEG, or related biosignals.
- Proven experience with time-series modeling, neural forecasting, multimodal learning, or brain-computer interface research.
- Proven experience with efficient attention or large-model training methods, including scaled-dot-product attention, FlashAttention, mixed precision, or activation checkpointing.
- A research record in machine learning, computer vision, natural language processing, computational neuroscience, biomedical AI, or a related area.
- Proven experience developing machine-learning or deep-learning models for research applications.
Principal Responsibilities
- User Training: Provide advanced training and guidance to users of the research core. 2. Experiment Support: Collaborate with researchers on experimental design, execution, data analysis, and troubleshooting within the research core. 3. Facility/Instrument Support: Manage and oversee research projects within the research core. 4. Technique Development: Lead the implementation and optimization of advanced techniques and methodologies and develop standard operating procedures for research core services. 5. Data Analysis/Interpretation: Analyze and interpret complex scientific data, generating meaningful insights and recommendations. 6. Equipment Procurement: Assist in the selection, acquisition, and maintenance of research core equipment. 7. Professional Development: Stay updated with emerging research trends and technologies. Participate in professional development activities and scientific conferences. 8. Scientific Participation: Contribute scientific data for manuscript preparation and grant proposals 9. Staff Training and Mentoring: Contribute to the training and mentoring of junior staff members. 10. Vendor Relations: Manage vendor relationships, negotiates contracts, and evaluates new technologies for optimal procurement processes. 11. Education and Outreach: Contribute to education and outreach activities within the core, including tours, events, etc. Required Education and Experience Bachelor’s degree in a related discipline and four years of related experience or an equivalent combination of skills, education, and experience.
Job Posting Date
10/14/2026 Job Category
Professional Bargaining Unit
NON Compensation Grade
Administration & Operations Compensation Grade Profile
Supervisor; Senior Associate (24) Salary Range
$68,000.00 - $120,500.00
Benefits
A general description of the benefits to be offered with this Managerial & Professional position is available at: Yale Benefits
Time Type
Full time Duration Type
Staff Fixed Duration (Fixed Term) Work Model
Hybrid Background Check Requirements
All candidates for employment will be subject to pre-employment background screening for this position, which may include motor vehicle, DOT certification, drug testing and credit checks based on the position description and job requirements. All offers are contingent upon the successful completion of the background check. For additional information on the background check requirements and process visit "Learn about background checks" under the Applicant Support Resources section of Careers on the It's Your Yale website.
Health Requirements
Certain positions have associated health requirements based on specific job responsibilities. These may include vaccinations, tests, or examinations, as required by law, regulation, or university policy.
Posting Disclaimer
Salary offers are determined by a candidate’s qualifications, experience, skills, and education in relation to the position requirements, along with the role’s grade profile and current internal and external market conditions.
The intent of this job description is to provide a representative summary of the essential functions that will be required of the position and should not be construed as a declaration of specific duties and responsibilities of the position. Employees will be assigned specific job-related duties through their hiring department.
The University is committed to basing judgments concerning the admission, education, and employment of individuals upon their qualifications and abilities and seeks to attract to its faculty, staff, and student body qualified persons from a broad range of backgrounds and perspectives. In accordance with this policy and as delineated by federal and Connecticut law, Yale does not discriminate in admissions, educational programs, or employment against any individual on account of that individual’s sex, sexual orientation, gender identity or expression, race, color, national or ethnic origin, religion, age, disability, status as a special disabled veteran, veteran of the Vietnam era or other covered veteran.
Inquiries concerning Yale's Policy Against Discrimination and Harassment may be referred to the Office of Institutional Equity and Accessibility (OIEA).
Note
Yale University is a tobacco-free campus.
Salary Context
This $68K-$120K range is in the lower quartile 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 Yale 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($94K) sits 56% below the category median. Disclosed range: $68K to $120K.
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.
Yale University AI Hiring
Yale University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New Haven, CT, US. Compensation range: $120K - $120K.
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
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