Machine Learning Engineer Intern

We need to output HTML fragment with sections: headings

for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real content. We need to extract content from job posting. We should not repeat job title as heading. No Job ID etc. Remove boilerplate like EEO, etc. Also remove application instructions. We need to preserve all substantive info. Let's parse the posting: - Job Type: Internship (maybe not needed as heading; could be in a short p) - Description: Why Work at Paylocity (some paragraphs) - Early Careers (some paragraphs) - Position Overview (some paragraphs) - In-Office: location (Rochester, NY) and mention internship programming (P&T internship summit in Schaumburg, IL) - Our Team Is (list of bullet-like sentences) - Primary Responsibilities (list of bullet points) - Required Qualifications (list) - Preferred Skills (list) - What You Will Learn (list) - Growth and Conversion Opportunity (paragraph) - Salary Description: $30.25/hr We need sections: About the role? Could be Position Overview. Responsibilities: Primary Responsibilities. Requirements/Qualifications: Required Qualifications. Skills: Preferred Skills. Benefits? Not explicitly listed; maybe early careers mentions award-winning training, one-on-one coaching, etc. Could be Benefits? The posting mentions "Benefit from award-winning training and one-on-one coaching". That's a benefit. Also maybe "Explore how you can go from the classroom to the conference room". Not exactly benefits. But we can include a Benefits section if we have content: award-winning training, one-on-one coaching, internship programming, etc. However we need to ensure we only include sections with real specific content. The posting does not have a dedicated Benefits heading, but we can extract benefit-like info from Early Careers and Position Overview. Might be okay to create a Benefits section with those details. Pay: Salary Description $30.25/hr -> we can include Pay section with that. Schedule: Not specified (In-Office: based in Rochester, New York. Interns expected to work on-site). That's schedule info: location and expectation of on-site. Could be Schedule section with location and on-site expectation. The posting says "In-Office: This role is intended to be based in Rochester, New York. Interns are expected to work on-site in the designated office environment and participate in Paylocity's internship programming, including the P&T internship summit in Schaumburg, Illinois." That's schedule/location info. So we can include Schedule section. We should not include sections with filler like "commensurate with experience". Not present. Now we need to format. We'll produce something like:

About the role

... (position overview + early careers maybe)

But we need to avoid duplication. Perhaps combine Position Overview and Early Careers into About the role. Let's extract substantive paragraphs: Why Work at Paylocity: description of company. Early Careers: description of early careers program. Position Overview: details about ML Engineering internship. Our Team Is: bullet-like sentences. We could put About the role as a summary of company and role. But we must preserve all substantive info; we can split into multiple sections: About the role (company description), Responsibilities, Requirements (Required Qualifications), Skills (Preferred Skills), Benefits (training, coaching, internship programming), Pay, Schedule. Let's craft each. About the role: combine Why Work at Paylocity, Early Careers, Position Overview, Our Team Is? Might be too much but okay. Better: About the role: company description and early careers. Position Overview could be part of About the role as well. But we need to ensure we don't lose info. Let's create sections: - About the role: includes Why Work at Paylocity, Early Careers, Position Overview, Our Team Is (maybe as a paragraph summarizing team focus). Could be a bit long but okay. - Responsibilities: Primary Responsibilities list. - Requirements: Required Qualifications list. - Skills: Preferred Skills list. - Benefits: award-winning training, one-on-one coaching, internship programming (P&T internship summit), opportunity to go from classroom to conference room, growth and conversion opportunity maybe. - Pay: $30.25/hr. - Schedule: In-office based in Rochester, NY; expected to work on-site; participate in internship programming including P&T internship summit in Schaumburg, IL. We must not repeat info unnecessarily but okay. Now format lists:

• for each bullet. We need to extract bullet points from Primary Responsibilities: - Contribute to the design, implementation, testing, and improvement of shared AI platform capabilities and services within a defined project scope. - Build on top of AI platform components to support new AI-powered product experiences, including agent workflows, model integrations, and reusable platform tooling. - Contribute to building or improving conversational interfaces and experiences, including request handling, conversation flows, context management, and integration with backend services or agents. - Help develop and improve AI observability and evaluation capabilities, including tracing, metrics, quality evaluation, debugging, and monitoring of AI or agent behavior. - Write clean, maintainable, and well-documented Python code following established engineering standards and best practices. - Collaborate with Data Science and Data Engineering partners to help integrate models and data workflows into reliable software solutions. - Contribute to automated testing, CI/CD workflows, monitoring, and troubleshooting for assigned ML engineering work. - Investigate bugs and technical issues with guidance, document findings, and participate in root-cause analysis and problem resolution. - Participate in code reviews, incorporate feedback, and learn how engineering teams balance quality, reliability, scalability, and delivery. - Participate actively in agile ceremonies, technical discussions, and cross-functional meetings. - Document technical decisions, implementation details, and lessons learned from internship projects. Required Qualifications list: - Currently pursuing a Master’s degree in Computer Science, Machine Learning, Data Science, Data Engineering, Software Engineering, Statistics, Mathematics, or a related quantitative or technical field. - Foundational programming experience in Python through coursework, academic projects, research, hackathons, or other practical work. - Foundational understanding of software engineering concepts such as data structures, algorithms, testing, version control, and debugging. - Coursework, academic, research, or project experience with AI/ML systems, including exposure to AI observability or evaluation concepts such as tracing, quality metrics, model or agent evaluation, prompt/response analysis, or related evaluation frameworks. - Familiarity with Git or another version control system. - Ability to work effectively in a collaborative, team-oriented environment and to seek guidance when needed. - Strong curiosity, attention to detail, and willingness to learn unfamiliar tools and technologies. - Ability to communicate technical ideas clearly in written and verbal discussions. Preferred Skills list: - Experience leveraging AI-assisted coding agents or developer tools such as Claude Code, Cursor, GitHub Copilot, or similar tools to support software development, debugging, testing, or technical problem solving. - Academic, research, internship, or personal project experience building machine learning applications in Python. - Exposure to data engineering or distributed computing concepts, including Spark, Databricks, or similar technologies. - Familiarity with cloud platforms such as AWS, GCP, or Azure. - Exposure to APIs, containers, CI/CD, Infrastructure as Code, or other modern software delivery practices. - Interest in generative AI, recommendation systems, ML platforms, MLOps, or responsible AI. - Contributions to open-source software or participation in technical clubs, hackathons, competitions, or research projects. What You Will Learn list: - How machine learning solutions move from experimentation into reliable, maintainable software. - How ML engineers collaborate with Data Scientists, Data Engineers, DevOps, security, and product partners. - How automated pipelines, testing, CI/CD, observability, and cloud infrastructure support production ML systems. - How to review code, receive technical feedback, debug issues, and communicate engineering tradeoffs. - How to balance model quality with software quality, scalability, reliability, security, and customer impact. - How responsible AI considerations are incorporated into the design and delivery of AI/ML features. Growth and Conversion Opportunity paragraph: - This internship is intended to help students with 0 years of professional experience build the skills needed for an early-career Machine Learning Engineering role. High-performing interns may be considered for future full-time opportunities, based on performance, business needs, and available headcount. We could put that under Benefits or maybe a separate section "Growth Opportunity". But we only have headings list; we could include it under Benefits as part of benefits. Benefits: award-winning training, one-on-one coaching, opportunity to go from classroom to conference room, internship programming (P&T internship summit), growth and conversion opportunity. Now About the role: need to include company description and early careers and position overview and team focus. Let's craft: Why Work at Paylocity: Paylocity is an award-winning provider of cloud-based HR and payroll software solutions, offering the most complete platform for the modern workforce. The company has become one of the fastest-growing HCM software providers worldwide by offering an intuitive, easy-to-use product suite that helps businesses automate and streamline HR and payroll processes, attract and retain talent, and build a strong workplace culture. While traditional HR and payroll providers automate basic HR processes such as payroll and benefits administration, Paylocity goes further by developing tools that HR and businesses need to compete for talent and deliver against the expectations of the modern workforce. Early Careers: Bring your talent and eagerness to learn to Paylocity, where you’ll discover the skills needed to launch your career! Benefit from award-winning training and one-on-one coaching as you play a key role in Paylocity’s future with an early careers position. Explore how you can go from the classroom to the conference room with internships and new-grad programs at one of Glassdoor's Best Places to Work. Experience the support that’ll take you from grad to a flourishing career with a position in Paylocity’s early careers division! Position Overview: Paylocity is growing its Machine Learning Engineering organization. Our Machine Learning Engineering team develops infrastructure and tooling that enable data-driven decisions and AI/ML experiences at scale for millions of Paylocity users. As a Machine Learning Engineer Intern in Product & Technology, you will contribute to well-scoped machine learning engineering work with mentorship and guidance from experienced engineers. You will gain hands-on experience building, testing, and improving ML software, data and modeling pipelines, and supporting infrastructure. You will collaborate closely with Machine Learning Engineers, Data Scientists, Data Engineers, DevOps, and other platform teams to learn how reliable ML solutions are designed and delivered in production environments. This internship is designed as a learning experience: you are not expected to independently own production systems or architecture. Instead, you will work on meaningful scoped projects, ask questions, incorporate feedback, and develop the engineering judgment needed for an early-career Machine Learning Engineer. Our Team Is: Building infrastructure that can power ML and AI features for millions of users. Building and deploying AI capabilities that help customers get more value from the Paylocity platform. Treating responsible AI and AI ethics as first-class considerations in our engineering processes. Working collaboratively across Machine Learning Engineering, Data Science, Data Engineering, DevOps, security, and platform teams. Invested in learning and applying modern machine learning engineering tools, technologies, and practices. We can combine these into About the role section. Now schedule: In-Office: This role is intended to be based in Rochester, New York. Interns are expected to work on-site in the designated office environment and participate in Paylocity's internship programming, including the P&T internship summit in Schaumburg, Illinois. Now Pay: Salary Description $30.25/hr. Now produce HTML. We must not include any markdown fences, just raw HTML. Let's construct. We'll start with

  About the role

  then

  ... maybe multiple paragraphs? Could combine into one paragraph but might be long. Could split into multiple

  for readability but still okay. The instruction: use

  for prose