Principal AI & Machine Learning Engineer - U.S. Based Hybrid Opportunity

We need to output HTML fragment with sections: About the role? Actually the posting includes "About Us" and then description. We need headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. But we only include sections that have real specific content. The posting includes "About Us" which is about organization, not exactly "About the role". Could map to "About the role"? The instruction: use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can use those headings if content exists. We have content for About Us (mission). Could treat as About the role? Might be okay. Responsibilities present. Requirements present (includes qualifications). There's also a separate "QUALIFICATIONS" subsection under Requirements. Could combine. There's also "The ideal candidate will possess:" which could be Qualifications or Skills. There's also a Pay range. Benefits present. No schedule info. So we include sections: About the role (using About Us content), Responsibilities, Requirements (maybe combine qualifications), Qualifications (maybe separate ideal candidate?), Skills (maybe list technical skills), Benefits, Pay. Avoid empty sections. We must not repeat job title as heading. No Job ID. Remove boilerplate like application instructions, scam warnings, EEO, etc. Also remove "How Do i Apply" and scam warning. Also remove navigation/legal etc. We must preserve all substantive information. So we need to keep the mission description, responsibilities bullet points, requirements bullet points, qualifications bullet points, ideal candidate bullet points, skills (maybe list of proficiencies), benefits list, pay range. We need to format using

headings,

• for lists,

  for prose. We must not invent or summarize away detail. Keep details. Let's extract content. About Us paragraph: "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." That's substantive. Responsibilities: The block after "Responsibilities". Let's capture sentences: "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." We can break into bullet points maybe each sentence as

• . But we can also keep as prose

  . The instruction: Use

  • for any list of duties/requirements/benefits. So we should turn responsibilities into list items. Similarly requirements list. Let's extract responsibilities as bullet points: - The Principal AI & 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. Now Requirements: The block after "Requirements QUALIFICATIONS". It includes a list of requirements. Let's parse. It says: "This role requires: Candidates must live in the United StatesHybrid Work & Travel: Employees can live anywhere in the US or its territories, with the willingness and ability to travel for periodic in-person events—including semi-annual Common App retreats, department retreats, and strategic leadership sessionsBachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related quantitative or technical field; or an equivalent combination of education and experience8-10 years of progressive experience in machine learning, AI engineering, or data science, with at least 3 years focused on applied AI and production ML systemsDemonstrated experience integrating LLMs and generative AI into enterprise analytic or operational workflowsDemonstrated experience working within enterprise data governance and compliance frameworksExpertise 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 monitoringProficiency in R and Python for machine learning and AI development; familiarity with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or equivalentExperience building and operationalizing LLM-powered applications, including prompt engineering, retrieval-augmented generation (RAG), and tool/agent orchestration frameworksStrong 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 environmentsKnowledge of AI governance, responsible AI principles, and compliance considerations for AI systems handling sensitive or personally identifiable dataProven ability to serve as a senior technical individual contributor, setting standards, making architectural decisions, and mentoring peers without formal management authorityStrong interpersonal and communication skills, with the ability to translate complex AI/ML concepts into accessible language for non-technical stakeholders and executive audiencesProven ability to manage multiple competing priorities and deliver high-quality AI solutions in a fast-paced, collaborative environment" We need to split into sensible bullet points. Also note there is a weird concatenation: "Candidates must live in the United StatesHybrid Work & Travel:" Should be separate. Let's extract each requirement: - Candidates must live in the United States. - Hybrid Work & Travel: Employees can live anywhere in the US or its territories, with the willingness and ability to travel for periodic in-person events—including semi-annual Common App retreats, department retreats, and strategic leadership sessions. - 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. Now after that there is "The ideal candidate will possess:" list. That could be Qualifications or Skills. We'll treat as Qualifications (maybe separate heading Qualifications). Let's extract: - 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. We could also treat some as Skills: e.g., proficiency in R and Python, etc. But we already have those in requirements. The ideal candidate list is more qualifications/preferred. We could have sections: Requirements (mandatory), Qualifications (preferred/ideal), Skills (maybe technical skills). But to avoid thin sections, we could combine Requirements and Qualifications into one Requirements section, but we have a lot. The instruction: Prefer a few substantive sections over many thin ones. So maybe we have About the role, Responsibilities, Requirements (including both mandatory and ideal), Benefits, Pay. That's 5 sections. We need to include Skills only if there is specific content not covered elsewhere. The ideal candidate list includes some skills but we can put under Requirements as "Preferred qualifications". That's okay. Thus sections: About the role, Responsibilities, Requirements, Benefits, Pay. We must not include Schedule (no info). Benefits we have list. Now we need to format Benefits list: The benefits block after "Benefits Common App is a virtual first environment...". Let's extract bullet points. Paragraph: "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 balanceVirtual-first officePaid Time Off (PTO)Seven company-wide holidaysNine floating holidays*Sick leaveMonthly mental health dayfloating holidays prorated depending on start dateVirtual-first supportChoice of PC of MAC laptopMay choose an external monitor, keyboard, mouse, and/or headsetOne-time office set-up stipendMonthly remote work stipendMonthly mobile stipendFinancial securityMarket-based salariesPerformance-based bonus403(b) retirement plan5% company contributionadditional 5% company match