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About This Role
ABOUT US
Do you have a passion for higher education? Do you want to make a positive impact on the college admissions process? Our staff help to remove barriers and encourage students to forge their path to a better future. Common App is a national not\-for\-profit organization dedicated to the pursuit of access, equity, and integrity in the college admission process. Each year we support more than 1 million students, one\-third of whom are first\-generation, as they apply to our more than 1100 diverse member colleges \& universities using the Common App's free online application.
If you are an experienced Artificial Intelligence professional and want to be part of a mission\-driven non\-profit that uses innovative technology to advance the college admission process, Common App may be a great match for you. Common App is currently searching for a Principal AI \& Machine Learning Engineer.
RESPONSIBILITIES
The Principal Artificial Intelligence (AI) \& Machine Learning (ML) Engineer is the technical leader responsible for embedding artificial intelligence into enterprise workflows and analytic processes to drive measurable gains in productivity, insight generation, and decision support within the Data Analytics \& Research (DAR) division. Highly regarded for exceptional performance and deep technical expertise, this role focuses on applied, production\-ready AI/ML, driving groundbreaking initiatives that embed artificial intelligence into enterprise workflows
Reporting to the Senior Director, Analytics Engineering, the Principal AI/ML Engineer partners closely with Data Engineering, Data Governance, and cross\-functional business stakeholders to ensure that AI solutions are scalable, secure, compliant, and aligned with enterprise data strategy. This role operates as a senior individual contributor and technical authority, setting standards for AI development practice, mentoring teammates on applied AI techniques, and translating complex machine learning capabilities into enterprise\-grade systems that deliver lasting organizational value. The Principal AI/ML Engineer is a recognized expert in the field, frequently sought out for technical guidance, mentorship, and thought leadership both within the organization and across the broader AI and higher education data community.
Requirements QUALIFICATIONS
This role requires:
- Candidates must live in the United States.
- Willing to travel to attend twice annual Common App Retreat.
- Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related quantitative or technical field; or an equivalent combination of education and experience.
- 8–10 years of progressive experience in machine learning, AI engineering, or data science, with at least 3 years focused on applied AI and production ML systems.
- Demonstrated experience integrating LLMs and generative AI into enterprise analytic or operational workflows.
- Demonstrated experience working within enterprise data governance and compliance frameworks.
- Expertise in applied machine learning and AI, including supervised and unsupervised learning, natural language processing, and large language model integration (e.g., OpenAI, Anthropic, open\-source LLMs via Hugging Face).
- Demonstrated experience designing and deploying production\-grade AI/ML systems, including end\-to\-end ML pipelines covering training, evaluation, versioning, deployment, and monitoring.
- Proficiency in R and Python for machine learning and AI development; familiarity with ML frameworks such as scikit\-learn, PyTorch, TensorFlow, or equivalent.
- Experience building and operationalizing LLM\-powered applications, including prompt engineering, retrieval\-augmented generation (RAG), and tool/agent orchestration frameworks.
- Strong SQL skills and experience working with large\-scale cloud data platforms (e.g., Databricks, Snowflake, BigQuery); ability to design AI\-ready data pipelines within enterprise warehouse environments.
- Knowledge of AI governance, responsible AI principles, and compliance considerations for AI systems handling sensitive or personally identifiable data.
- Proven ability to serve as a senior technical individual contributor, setting standards, making architectural decisions, and mentoring peers without formal management authority.
- Strong interpersonal and communication skills, with the ability to translate complex AI/ML concepts into accessible language for non\-technical stakeholders and executive audiences.
- Proven ability to manage multiple competing priorities and deliver high\-quality AI solutions in a fast\-paced, collaborative environment.
The ideal candidate will possess:
- Master’s or doctoral degree in Computer Science, Machine Learning, Data Science, or a related field.
- Relevant AI/ML or cloud certifications (e.g., Databricks Certified Machine Learning Professional, AWS Machine Learning Specialty, or equivalent).
- Experience working with Agile frameworks for cross\-functional, product\-minded collaboration.
- Experience with agentic AI frameworks.
- Familiarity with Databricks AI features, including MLflow, Model Serving, and Genie or semantic layer integrations.
- Experience in higher education, nonprofit, or mission\-driven technology contexts.
- Advanced training in statistics, causal inference, or program evaluation methods.
- A passion for higher education is a plus.
PAY RANGE
- $144,160 \- $162,180
Benefits
Common App is a virtual first environment. We value our employees’ time and efforts. Our commitment to your success is enhanced by our competitive salary and an extensive benefits package including:
- Work\-Life balance
- + Virtual\-first office
+ Paid Time Off (PTO)
+ Seven company\-wide holidays
+ Nine floating holidays\*
+ Sick leave
+ Monthly mental health day
- floating holidays prorated depending on start date
- Virtual\-first support
- + Choice of PC of MAC laptop
+ May choose an external monitor, keyboard, mouse, and/or headset
+ One\-time office set\-up stipend
+ Monthly remote work stipend
+ Monthly mobile stipend
- Financial security
- + Market\-based salaries
+ Performance\-based bonus
+ 403(b) retirement plan
+ - 5% company contribution
- additional 5% company match
- 3\-year vesting schedule
- Participation may begin immediately
- Health \& wellness
- + Choice of two health insurance plans
+ - Health Savings Account, depending on health plan selection
- Medical Flexible Savings Account, depending on health plan selection
+ Vision insurance
+ Dental insurance
+ Insurance coverage begins on the date of hire
+ Dependent Care Flexible Spending Account
+ Maven virtual clinic for women’s and family health
+ Company provided life and ad\&d insurance
+ Opportunity to purchase additional life insurance for self, spouse, and dependents
+ Company provided short and long\-term disability insurance
- Career development
- + Budgeted annual funds for professional development
+ Growth opportunities within the company
- Additional perks
- + Mutual of Omaha Employee Assistance Program
+ Mutual of Omaha will preparation services
+ Mutual of Omaha travel assistance
+ Payroll dedication pet insurance through PinPaws
+ 1Password family account
We work to maintain the best possible environment for our staff, where people can learn and grow. We strive to provide a diverse, collaborative, team\-oriented, creative environment where each person feels encouraged to contribute to our processes, decisions, planning, and culture.
HOW DO I APPLY
To apply for this opportunity, send your resume and cover letter with salary expectations.
PROTECTING YOUR PERSONAL INFORMATION:
During the recruiting process, please note that Common App will never:
- Provide a job offer without an interview
- Ask for payment to process documents, purchase equipment or for any other reason
- Request banking or credit card information
- Direct you to third\-party services to obtain visas or other documentation
As we work alongside you through our recruitment process, please remain alert and never provide financial information or payment to anyone claiming to offer a job opportunity.
If you believe you’re a victim of a job scam, report it to the Federal Trade Commission (FTC) or your state attorney general. To learn more about job scams, read the FBI’s public service announcement or visit the FTC site.
Salary Context
This $144K-$162K range is below the median 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 Common App, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($153K) sits 29% below the category median. Disclosed range: $144K to $162K.
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.
Common App AI Hiring
Common App has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Arlington, VA, US. Compensation range: $162K - $162K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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