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San Francisco, CA · Remote
$123K - $169K/yr
This position is 100% Remote. Senior Machine Learning Engineer Responsibilities: - Build core Machine Learning (ML) systems that power a proactive, long-horizon Artificial Intelligence (AI) product ...
Quick apply
Senior Machine Learning Engineer, Artificial Intelligence (AI) Required, Work From Home
San Francisco, CA · Remote
$123K - $169K/yr
This position is 100% Remote. Senior Machine Learning Engineer Responsibilities: - Build core Machine Learning (ML) systems that power a proactive, long-horizon Artificial Intelligence (AI) product ...
Remote Machine Learning Engineer information
See salary details
$31.5K - $46.2K
1% of jobs
$46.2K - $61K
1% of jobs
$61K - $75.7K
5% of jobs
$75.7K - $90.4K
6% of jobs
$102.6K is the 25th percentile. Wages below this are outliers.
$90.4K - $105.1K
14% of jobs
$105.1K - $119.9K
14% of jobs
The median wage is $127.2K / yr.
$119.9K - $134.6K
18% of jobs
$134.6K - $149.3K
14% of jobs
$152.3K is the 75th percentile. Wages above this are outliers.
$149.3K - $164K
12% of jobs
$164K - $178.8K
11% of jobs
$178.8K - $193.5K
5% of jobs
$31.5K
$128.8K
$193.5K
How much do remote machine learning engineer jobs pay per year?
What engineers make $300,000 a year?
What are some typical challenges faced by Remote Machine Learning Engineers, and how are they addressed?
Remote Machine Learning Engineers often face challenges such as coordinating across different time zones, ensuring smooth communication with team members, and accessing large datasets or secure environments remotely. Organizations commonly address these by using robust collaboration tools (like Slack, GitHub, and Jira), establishing clear documentation, and setting regular virtual meetings to maintain alignment. Many companies also provide secure remote environments or VPN access for handling sensitive data and code. Proactive communication and organized workflows help mitigate these challenges, enabling engineers to remain productive and connected to their teams.
What engineers make $500,000?
Will MLE be replaced by AI?
What are the key skills and qualifications needed to thrive in the Remote Machine Learning Engineer position, and why are they important?
To thrive as a Remote Machine Learning Engineer, you need a strong background in computer science, mathematics, and experience with machine learning algorithms, typically supported by a relevant degree and prior project work. Proficiency with programming languages like Python, machine learning frameworks such as TensorFlow or PyTorch, and familiarity with cloud computing platforms is crucial, and certifications like AWS Certified Machine Learning can enhance your profile. Excellent communication, self-motivation, and time-management skills are also essential for collaborating across remote teams and meeting project goals. These combined technical and soft skills are vital for developing effective machine learning solutions while ensuring productivity and collaboration in a virtual work environment.
What is a Remote Machine Learning Engineer job?
A Remote Machine Learning Engineer designs, develops, and deploys machine learning models while working from a remote location. They preprocess data, train and optimize models, and integrate them into production systems. Their role often involves collaborating with data scientists, software engineers, and stakeholders to solve complex problems using AI. Strong programming skills in Python, experience with ML frameworks like TensorFlow or PyTorch, and cloud computing knowledge are essential. Remote ML engineers must also communicate effectively and manage their time efficiently to work asynchronously with teams.
Can ML engineers work remotely?
- Full Time No Experience Machine Learning
- Machine Learning Engineer Associate
- Director Google Machine Learning Engineer
- Freelance Google Machine Learning Engineer
- Full Time Tesla Machine Learning Engineer
- Reinforcement Learning Engineer
- Machine Learning Operations Engineer
- Sr Machine Learning Engineer
- Senior Machine Learning Ops Engineer
- Remote Machine Learning Compiler Engineer

Full-time
Medical, Dental, Vision, Life, Retirement
Posted 25 days ago
Paylocity rating
7.6
Based on 68 frontline employees who took The Breakroom Quiz
164th of 454 rated business services
Job description
Full-time
Description
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.
Help Paylocity enhance communication and enable employees to connect, collaborate, and create from anywhere with a position in Product & Technology!
Want to develop the strategies and principles needed to deliver compelling software? Join our team and help us enhance our all-in-one software platform, elevate our one-of-a-kind technology, and improve the employee experience.
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.
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 Staff 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 of 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
- 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 8 years of machine learning engineering or similar experience at software companies; or, advanced degree (master's or PhD) in machine learning engineering, data engineering, computer science, engineering, statistics, mathematics, data science, or other quantitative field, with 3 years of demonstrated machine learning engineering success or similar experience.
- 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. • 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
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 accessibility@paylocity.com. 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 $146,600 - $209,400 /yr; however, base pay offered may vary depending on job-related knowledge, skills, and experience. This position is eligible for an annual bonus and restricted stock unit grant based on individual performance in addition to 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.
What Paylocity employees say
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Benefits
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About Paylocity
Sourced by ZipRecruiter
Industry
Software development
Company size
5,001 - 10,000 Employees
Headquarters location
Schaumburg, IL, US
Year founded
1997