Principal Machine Learning System Engineer (AI)

TL;DR

Principal Machine Learning System Engineer (AI): Building and maintaining core infrastructure for machine learning engineers and data scientists to develop, train, deploy, and operate ML models and pipelines with an accent on solving difficult problems and tackling complex infrastructure and architecture challenges. Focus on driving involved projects from technical design to launch and collaborating with other teams to set expectations and communicate results.

Location: Remote within the United States

Salary: USD 232,200 - USD 303,150 (Zone A)

Company

hirify.global is motivated by a common goal: to unleash the potential of every team through its software products and solutions designed for all types of work.

What you will do

• Build and maintain the core infrastructure to allow machine learning engineers and data scientists to develop, train, evaluate, deploy, and operate Machine Learning models and pipelines.
• Solve difficult problems, tackling complex infrastructure and architecture challenges.
• Lead other engineers to drive involved projects from technical design to launch.
• Collaborate with other teams and internal customers to set expectations, gather input, and communicate results.

Requirements

• 5+ years of experience as a software developer.
• Fluency in at least one modern object-oriented programming language (preferably Java/Kotlin and Python).
• Experience with Continuous Delivery and Continuous Integration.
• Experience building and operating large-scale distributed systems using Amazon Web Services (S3, Kinesis, Cloud Formation, EKS, AWS Security and Networking).
• Experience with distributed large-scale data processing (preferably Apache Spark).
• Basic understanding of Machine Learning projects lifecycle.

Nice to have

• Expert-level with search platform, deep learning training/inference platform.
• Experience with Databricks.
• Experience with scaling and deploying Machine Learning models.

Culture & Benefits

• hirify.globals can choose where they work – whether in an office, from home, or a combination of the two.
• Health and wellbeing resources.
• Paid volunteer days.