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About This Role
Overview:
The Accelerator seeks a Data/ML Engineer to strengthen our data team and advance the engineering, enrichment, and provisioning of the data we collect.
The Accelerator at Princeton includes a portfolio of multiple planned independent and intersecting tools, built on a shared data and compute platform serving computational social scientists at research institutions across North America, Europe, and Africa. The Data/ML Engineer will work within our team to help drive data engineering and machine learning initiatives and collaborations. They will play a crucial role in building and operating the pipelines that transform large-scale social media and web behavior data into research-ready data products, and in developing the machine learning and enrichment capabilities that extend their value. They will work on problems that have no precedent and little source material, requiring novel solutions. They will also be responsible for working with the other teams within the Accelerator and our external partners to help foster collaboration and create an impactful environment for our users. This is a 6-month term role with potential for extension. A remote work arrangement within the United States may be considered for candidates with the appropriate background and experience. Responsibilities: Strategy:* Work closely with the Accelerator leadership team to align data engineering and machine learning initiatives with overarching goals and long-term vision.
- Identify and prioritize development projects that benefit from data engineering and machine learning methodologies and innovations.
Data Engineering:* Design, build, and operate data pipelines across the Accelerator's medallion architecture, with end-to-end ownership of transformation layers that serve researchers.
- Ensure the accuracy, integrity, and quality of data to be made available through the Accelerator, including data quality validation at pipeline boundaries and enforcement of versioned schema contracts.
- Diagnose and optimize distributed data processing workloads at production scale.
- Develop deployment automation, CI/CD, and release processes for data products, including versioned data releases and researcher-facing change documentation.
Machine Learning and Data Science:* Design, develop, and operate ML and NLP enrichment pipelines over large-scale text and behavioral data, including language identification, translation, and topic and content classification.
- Own the full lifecycle of enrichment models: selection, evaluation against labeled data, batch inference architecture, cost efficiency, and reprocessing and versioning strategy.
- Develop ML-ready feature layers and data products to support advanced research use cases.
- Evaluate and apply large language model workflows and other emerging AI methods where they demonstrably improve outcomes, with attention to their validity for downstream scientific analysis.
- Apply statistical analysis and modeling to characterize datasets, estimate coverage, and support research design.
Platform Operations and Cost Engineering:* Contribute to cost attribution, visibility, and governance across institutional workspaces, including cluster policies, budget controls, and storage lifecycle management.
- Design data and ML workloads to operate within the platform's cost governance framework.
- Develop automation for workspace and project provisioning as institutions and research projects onboard.
- Operate within Unity Catalog governance, multi-tenant isolation, and research data security requirements.
Research and Collaboration:* Work effectively in a modern, professional software and data engineering environment with a strong understanding of Agile concepts and practices.
- Modern Software Engineering Foundations: agile (Scrum), DevOps, CI/CD, code review, and pair programming, with working knowledge of cloud compute platforms to support collaborative, scalable, and efficient development.
- Author and maintain researcher-facing documentation and provide direct technical support to research users of the platform.
- Collaborate with research teams to define data products, sampling frames, and enrichment requirements, and apply state-of-the-art techniques to ongoing scientific challenges.
- Stay current with the latest advancements in data engineering, machine learning, and relevant fields to continuously innovate.
- Build strong relationships with external partners, driving collaborations that enhance the Accelerator's scientific impact.
Qualifications: Essential Qualifications:* 3+ years of relevant experience as a data engineer, machine learning engineer, or data scientist, which may include graduate research and internship experience, with a record of building production systems that operate reliably at scale. Experience working in a remote, agile environment.
- Bachelor's degree or equivalent in a relevant field.
- Strong proficiency in Python and SQL, and hands-on experience with distributed data processing (e.g., Apache Spark) on large data volumes.
- Experience building, evaluating, and operating machine learning or NLP pipelines, including batch inference.
- Working knowledge of cloud data platforms.
- Strong communication and interpersonal skills to effectively collaborate with researchers in the field, other engineers at various levels of experience, and administrative and leadership team members.
Preferred Qualifications:* Experience with Azure and Databricks, including Unity Catalog.
- Experience with infrastructure-as-code (e.g., Terraform), containers, and CI/CD tooling.
- Experience with large-scale social media, web behavior, or text-as-data research.
- Familiarity with large language model annotation workflows and their evaluation.
- Publications in reputable scientific journals or conferences is desirable.
Princeton University is an Equal Opportunity Employer and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law.
The University considers factors such as (but not limited to) scope and responsibilities of the position, candidate's qualifications, work experience, education/training, key skills, market, collective bargaining agreements as applicable, and organizational considerations when extending an offer. The posted salary range represents the University's good faith and reasonable estimate for a full-time position; salaries for part-time positions are pro-rated accordingly.
If the salary range on the posted position shows an hourly rate, this is the baseline; the actual hourly rate may be higher, depending on the position and factors listed above.
The University also offers a comprehensive benefit program to eligible employees. Please see this link for more information. Standard Weekly Hours: 36.25 Eligible for Overtime: No Benefits Eligible: Yes Probationary Period: 180 days Essential Services Personnel (see policy for detail): No Physical Capacity Exam Required: No Valid Driver’s License Required: No Experience Level: Mid-Senior Level : #Ll-DP1 Salary Range: $120,000 to $135,000
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 Princeton 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.
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.
Princeton University AI Hiring
Princeton University has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Princeton, NJ, US, Princeton, NJ.
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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