Role Description We are looking for a Learning Experience Lead to design end-to-end learning experiences, turning business and learning goals into effective educational solutions. In this role, you'll bring deep instructional design expertise, experiment with new approaches, and share your knowledge to help the methodology team continuously improve - without direct people management. - Own the end-to-end design of learning experiences — from identifying the opportunity to measuring learning outcomes. - Design complete courses from scratch, especially in emerging AI and other fast-changing domains where proven playbooks don't exist. - Leverage AI as a core part of your instructional design workflow — from research and prototyping to content creation, validation, and iteration. - Choose and justify the most effective learning approaches based on learner needs and business goals, rather than executing predefined briefs. - Define success metrics, instrument them, analyze results, and continuously improve your solutions based on learner behavior and outcomes. - Rapidly prototype, experiment, and iterate on new educational concepts using AI-first workflows and vibe coding. - Mentor other Learning Experience Designers, raising the team's overall quality and capability. - Identify ineffective instructional design patterns, improve them, and establish scalable best practices. - Define, document, and evolve instructional design standards across the team. Qualifications - 3+ years of hands-on instructional design experience, including experience in IT education. - Proven track record of independently shipping end-to-end educational products or courses, from concept to launch. - Strong AI literacy: actively uses AI tools to research, design, prototype, produce, and improve learning experiences. - Comfortable working in ambiguous environments and designing learning solutions for rapidly evolving domains, especially AI. - Experience defining learning success metrics and using data to continuously iterate on educational products. - Able to select and defend instructional strategies and learning formats based on learner research and business context. - Demonstrated ownership mindset with a high level of autonomy, accountability, and creative problem-solving. - Experience mentoring peers, sharing expertise, and raising instructional design quality across a team. - Passion for experimentation, rapid iteration, and building better learning experiences through continuous discovery. Benefits - High autonomy to explore, test, and improve processes. - Competitive compensation. - Fully remote collaboration. - Work alongside an experienced, global professional team.

• Design and fine-tune Large Language Model (LLM) pipelines • Implement semantic query architectures for documentation • Develop time-series forecasting models for supply chain • Build classification and anomaly detection models • Containerize models and deploy into secure environments • Optimize model inference and monitoring systems

• Collaborate closely with internal teams such as Data Science, Data Engineering, Paylocity’s Cloud Center of Excellence (CCOE), DevOps, and Delivery Platforms to understand requirements and ensure alignment of machine learning engineering solutions with overall business objectives and priorities. • Leverage cutting-edge big data technologies on AWS utilizing Databricks and Spark to develop scalable and efficient machine learning solutions for millions of users. • Create automated data and modeling pipelines, collaborating with internal teams to ensure smooth integration and deployment of machine learning software features. • Lead the optimization of CI/CD workflows, ensuring scalability and resilience while addressing complex challenges in automation in partnership with DevOps and Delivery Platforms. • Proactively identify and resolve issues/bugs, ensuring AppSec vulnerabilities are identified and corrected, working closely with Application Security and CCOE teams. • Drive the adoption of best practices in machine learning engineering across teams, contributing to the development of formal training programs and materials for MLE tool adoption. • Actively participate in cross-functional meetings and discussions, providing feedback, commentary, requirements, and questions to ensure alignment and drive project success.

• Collaborate closely with internal teams such as Data Science, Data Engineering, Paylocity’s Cloud Center of Excellence (CCOE), DevOps, and Delivery Platforms to understand requirements and ensure alignment of machine learning engineering solutions with overall business objectives and priorities. • Leverage cutting-edge big data technologies on AWS utilizing Databricks and Spark to develop scalable and efficient machine learning solutions for millions of users. • Create automated data and modeling pipelines, collaborating with internal teams to ensure smooth integration and deployment of machine learning software features. • Lead the optimization of CI/CD workflows, ensuring scalability and resilience while addressing complex challenges in automation in partnership with DevOps and Delivery Platforms. • Proactively identify and resolve issues/bugs, ensuring AppSec vulnerabilities are identified and corrected, working closely with Application Security and CCOE teams. • Drive the adoption of best practices in machine learning engineering across teams, contributing to the development of formal training programs and materials for MLE tool adoption. • Actively participate in cross-functional meetings and discussions, providing feedback, commentary, requirements, and questions to ensure alignment and drive project success.