Interested in this AI/ML Engineer role at Paylocity?
Apply Now →Skills & Technologies
About This Role
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.
We give our employees what they need to succeed, including great benefits and perks! We offer medical, dental, vision, life, disability, and a 401(k) match, as well as perks that support you, your family, and your finances. And if it’s career development you desire, we provide that, too! At Paylocity, people matter most and have always been at the heart of our business.
Take your career to the next level at one of G2's Top 100 Software Companies. Explore our Product \& Technology positions to see where you fit!
This is a fully remote position, allowing you to work from home or location of record within the U.S. with no in\-office requirements. You must be available five days per week during designated work hours. The work arrangement for this role is subject to change based on business needs and individual performance. This may include adjustments to on\-site requirements or schedule expectations, as necessary.
Machine Learning Engineer
Position Overview
Paylocity is growing its Machine Learning Engineering organization! Our machine learning engineering team is responsible for developing infrastructure and tooling to help enable data driven decisions and insights at scale for millions of Paylocity users.
As a Machine Learning Engineer in Product \& Technology, you will help Paylocity build and deploy Machine Learning solutions, to help our teams build better products faster, more reliably, and at the scale we see in production for our customers. We develop machine learning models and infrastructure to support internal team strategies and collaborate closely with our data science organization to drive efficiency and best practices. Your primary focus will be to leverage your expertise in software development, machine learning algorithms, and data infrastructure to architect, develop, and optimize machine learning solutions. You will play a key role in driving the development of scalable and efficient machine learning models, contributing to the enhancement of product features, and the overall improvement of our infrastructure.
Our team is:
- Building infrastructure that can power ML and AI features for millions of users
- Building and deploying platform\-wide recommendations to help companies follow HR best practices and allow employees to get the most out of our platform (Paylocity AI page)
- Baking AI Ethics into all our processes as a first\-class citizen (Blog Post)
- Working in a collaborative fully remote environment with a desire to share ideas and continuously improve
- Invested in staying current in machine learning engineering by applying the newest tools, technologies, and practices
- Excited to work on cutting\-edge technology!
Primary Responsibilities
The below represents the primary duties of the position, others may be assigned as needed. To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- 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.
Education and Experience
The below represents the primary duties of the position, others may be assigned as needed. To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- Bachelor’s degree with at least 3 years of machine learning engineering success or similar experience at software companies; or, advanced degree (master’s or PhD) preferred in machine learning engineering, data engineering, computer science, engineering, statistics, mathematics, data science, or other quantitative field, with no additional experience required.
- Experience in building production\-grade machine learning models and infrastructure in Python.
- Strong background in advanced Python and big data technologies
- Experience with cloud infrastructure (i.e., AWS, GCP, or Azure).
- Demonstrated experience with Infrastructure as Code (IAC) tools (i.e. CDK, Pulumi, etc.).
- Demonstrated ability to leverage machine learning engineering to drive business results.
- Skilled at translating business problems into machine learning engineering problems and communicating the results to non technical audiences.
- Able to work in a collaborative environment with a desire to share your ideas.
- Able to work independently and complete tasks with high quality, but unafraid to seek out suggestions from other team members.
- Strong understanding of data engineering and software engineering fundamentals.
- Self\-motivated, adaptable, and highly detail oriented.
Preferred Skills
- Professional or academic experience in HR, social science or psychology
- Contributions to open\-source software in Python
- Enthusiastic about how machine learning and infrastructure can lead to a superior customer experience.
- Be invested in staying current in machine learning and infrastructure by applying new technologies and practices
Physical requirements
- Ability to sit for extended periods: The role requires sitting at a desk or workstation for long periods, typically 7\-8 hours a day.
- Use of computer and phone systems: The employee must be able to operate a computer, use phone systems, and type. This includes using multiple software programs and inquiries simultaneously.
Paylocity is an equal\-opportunity employer. Paylocity is committed to the full inclusion of all individuals. We recruit, train, compensate, and promote regardless of race, religion, color, national origin, sex, disability, age, veteran status, and other protected status as required by applicable law. At Paylocity, we believe diversity makes us better.
We embrace and encourage our employees’ differences in age, culture, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion or spiritual belief, sexual orientation, socio\-economic status, veteran status, and other characteristics that make our employees unique. We actively cultivate these differences through our employee resource groups (ERGs), employee experiences, perspectives, talents, and approaches to drive innovation in the software and services we provide our customers.
We comply with federal and state disability laws and make reasonable accommodations for applicants and employees with disabilities. To request reasonable accommodation in the job application or interview process, please contact [email protected]. This email address is exclusively designated for such requests, aligning with federal and state disability laws. Please do not send resumes to this email address, as they will be removed.
The base pay range for this position is $106,600 \- $152,300/yr; however, base pay offered may vary depending on job\-related knowledge, skills, and experience. This position offers a full range of benefits outlined here . This information is provided per the relevant state and local pay transparency laws for the location in which this position will be performed. Base pay information is based on market location. Applicants should apply via www.paylocity.com/careers.
Salary Context
This $106K-$152K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Paylocity, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($129K) sits 41% below the category median. Disclosed range: $106K to $152K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Paylocity AI Hiring
Paylocity has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $152K - $272K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.