Staff Machine Learning Infrastructure Engineer
We are seeking a seasoned machine learning infrastructure engineer to join Rippling’s newly formed Applied Machine Learning team. As an engineer working on practical applications of large language models (LLMs), you will own the design and implementation of the infrastructure that trains, serves and integrates these models into our products.
What joining our team now means
Rippling’s Applied Machine Learning efforts are nascent. If you join us now, you’ll be an early team member and help shape:
- 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.
You are an expert at building ML infrastructure – having 4+ years of industry experience building infrastructure that handled data preprocessing/transformation, feature engineering/storage and model training/evaluation/deployment/serving.
You are a seasoned software engineer – having 8+ years of industry experience building software at some (or all) levels of the stack (foundational infra, backed, ux). You should be able to point to specific products that exist today that wouldn’t have been possible without your contribution.
You are comfortable with hands-on programming – Rippling mostly builds in Python, but prior experience in Python is not a hard requirement for this role ( jvm languages/go/ruby/typescript experience should be transferable).
You have a knack for communicating complex technical ideas with clarity and precision
Experience developing user-facing applications that use large language models (LLMs).
Experience with full stack software engineering (distributed systems, services, UX). The more of the stack you can span comfortably, the more effectively you’ll be able to help drive project outcomes.
Familiarity with LLM pre-training and/or fine-tuning techniques and infrastructure.
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.