Manager, Machine Learning Engineering

Paylocity

. Lead Machine Learning Engineering teams developing and enhancing machine learning infrastructure and solutions impacting millions of employees .

Posted 10/23/2026full-timeRemote • United StatesMid-LevelSenior💰 $185,400 - $344,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading Machine Learning Engineering teams and developing scalable AI platforms, with a strong focus on end-to-end ML tooling, automation, and best practices. Proven ability to mentor teams, influence business strategy through data insights, and ensure alignment with organizational objectives.

Highest-signal resume keywords

Machine Learning Infrastructure DevelopmentPython Programming for ML ModelsAWS Cloud Infrastructure (Glue, EMR, Lambda)Team Leadership and MentoringData-Driven Business Strategy Influence

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning EngineeringAI Platform DevelopmentData Pipeline AutomationModel Deployment and MonitoringInfrastructure-as-Code ToolingProduction-Grade ML ModelsContinuous Integration and DeliveryData Science CollaborationBest Practices for ML GovernanceQuantitative Analysis

Soft Skills

Coaching and MentoringExceptional Communication SkillsAdaptabilityDetail-OrientedCuriosity and Willingness to Learn

Tools & Technologies

GitHub ActionsTeamCityOctopusJenkinsAWS GlueAWS EMRAWS LambdaCloud-Based Source Code ManagementContinuous Integration ToolsML Tool Adoption Training

Industry Keywords

Machine Learning Best PracticesAI/ML TechnologiesData EngineeringCloud Center of ExcellenceArchitecture Review Board

Tech Stack

Tools & technologies

AWSCloudJenkinsPython

About the role

Key responsibilities & impact

Lead Machine Learning Engineering teams developing and enhancing machine learning infrastructure and solutions impacting millions of employees
Build and evolve AI platforms that scale across products, domains, and teams
Own and drive end-to-end ML tooling and automation, including infrastructure, data pipelines, model deployment, monitoring, and optimization
Collaborate with data science and engineering teams to drive adoption of MLE best practices
Contribute to formal training programs and materials for MLE tool adoption
Collaborate with Product & Technology teams, including Data Engineering, Cloud Center of Excellence, Platform teams, and Architecture Review Board
Influence business strategy using data and metrics and present recommendations and insights to Director-level and above leadership
Mentor, coach, and develop teams, fostering technical excellence, professional growth, and leadership capabilities
Oversee multiple projects, ensuring alignment with company objectives, resource optimization, and timely delivery
Establish best practices for scalable and reproducible machine learning development, deployment, and governance
Assess team outcomes, processes, and quality to drive continuous improvement
Keep the team current with advances in AI/ML technologies, methodologies, and best practices

Requirements

What you’ll need

Bachelor's degree in a quantitative field is required
5+ years of hands-on AI/ML success at software companies
2+ years of experience managing and leading data teams
Demonstrated success in coaching, mentoring, and developing talent
Experience writing production-grade machine learning infrastructure and/or models in Python
Hands-on experience with AWS cloud infrastructure, including Glue, EMR, and Lambda
Experience with infrastructure-as-code tooling
Familiarity with cloud-based source code management, continuous integration, continuous delivery, and software development best practices
Familiarity with GitHub Actions, TeamCity, Octopus, Jenkins, or similar tools
Ability to lead high-performing teams, inspire others, and drive business results
Ability to translate business problems into data/ML problems and communicate results to non-technical audiences
Self-motivated, adaptable, and highly detail-oriented
Strong curiosity and willingness to learn
Exceptional verbal and written communication skills
Must be available five days per week during designated work hours
Ability to sit for extended periods, typically 7–8 hours per day
Ability to operate computers and phone systems, type, and use multiple software programs simultaneously

Benefits

Comp & perks

Medical insurance
Dental insurance
Vision insurance
Life insurance
Disability insurance
401(k) match
Perks supporting employees, families, and finances
Career development opportunities
Annual bonus
Restricted stock unit grant based on individual performance
Fully remote work from home or location of record within the U.S.
No in-office requirements