Rippling is the first way for businesses to manage all of their HR & IT—payroll, benefits, computers, apps, and more—in one unified workforce platform.

By connecting every business system to one source of truth for employee data, businesses can automate all of the manual work they normally need to do to make employee changes. Take onboarding, for example. With Rippling, you can just click a button and set up a new employees’ payroll, health insurance, work computer, and third-party apps—like Slack, Zoom, and Office 365—all within 90 seconds.

Based in San Francisco, CA, Rippling has raised $1.2B from the world’s top investors—including Kleiner Perkins, Founders Fund, Sequoia, Bedrock, and Greenoaks—and was named one of America’s best startup employers by Forbes (#12 out of 500).

About the Role

We are seeking a highly skilled and experienced Senior Staff Machine Learning Engineer to join our team. As an engineer working on large language models (LLMs) at Rippling, you will work closely with product teams to build tools that people use. The work is necessarily cross-functional, and successful individuals on our team have an unusually high degree of ownership. Your expertise will contribute to the advancement of our organization's machine learning capabilities and drive innovation in our products and services.

What you will do

Collaborate with cross-functional teams to translate business requirements into models.
Design and develop scalable machine learning pipelines for data preprocessing, feature engineering, model training, and evaluation. You will work with data engineers to collect and preprocess data sets for model training.
Implement models in our production code base (primarily Python, Go).
Stay up-to-date with the latest research in ML and related fields, and apply this knowledge to improve Rippling products.

Qualifications

Ph.D. or equivalent in Computer Science, Engineering, Mathematics, or related field AND 4 or more years full-time Software Engineering work experience; OR
7 years full-time Software Engineering work experience, which includes 4+ years of software engineering experience in one or more of the following areas: advertising, recommendation systems, risk/fraud modeling, or natural language processing.
Comfortable with hands-on programming (eg, Python, Go, Java, C/C++)
Ability to communicate complex technical ideas with clarity and precision.

Additional Qualifications

Experience with developing things that use large language models (LLMs) and familiarity with pre-training and fine-tuning techniques.

(This job description was written with an assist from ChatGPT)

Additional Information

Rippling is an equal opportunity employer. We are committed to building a diverse and inclusive workforce and do not discriminate based on race, religion, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, age, sexual orientation, veteran or military status, or any other legally protected characteristics, Rippling is committed to providing reasonable accommodations for candidates with disabilities who need assistance during the hiring process. To request a reasonable accommodation, please email accomodations@rippling.com

Rippling highly values having employees working in-office to foster a collaborative work environment and company culture. For office-based employees (employees who live within a 40 mile radius of a Rippling office), Rippling considers working in the office, at least three days a week under current policy, to be an essential function of the employee's role.

This role will receive a competitive salary + benefits + equity. The salary for US-based employees will be aligned with one of the ranges below based on location; see which tier applies to your location here.

A variety of factors are considered when determining someone’s compensation–including a candidate’s professional background, experience, and location. Final offer amounts may vary from the amounts listed below.

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