Machine Learning Engineer Intern

PAYLOCITY CORPORATION•Town of Pittsford, NY

5d•Onsite

About The Position

Paylocity is expanding its Machine Learning Engineering team, which focuses on developing infrastructure and tooling for large-scale, data-driven decisions and AI/ML experiences for millions of users. As a Machine Learning Engineer Intern, you will work on well-scoped machine learning engineering tasks under the guidance of experienced engineers. You will gain practical experience in building, testing, and enhancing ML software, data and modeling pipelines, and supporting infrastructure. Collaboration with Machine Learning Engineers, Data Scientists, Data Engineers, DevOps, and other platform teams will provide insights into the design and delivery of reliable ML solutions in production. This internship is primarily a learning experience, not an expectation to manage production systems independently. You will focus on meaningful projects, ask questions, integrate feedback, and develop essential engineering judgment for an early-career Machine Learning Engineer. The role is based in Rochester, New York, requiring on-site work and participation in internship programs, including a summit in Schaumburg, Illinois. The team is dedicated to building scalable ML/AI infrastructure, deploying AI capabilities for customer value, prioritizing responsible AI and ethics, fostering cross-functional collaboration, and continuously learning modern ML engineering tools and practices.

Requirements

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.

Nice To Haves

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.

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.