Global-Scale ML Infra Engineer

TikTok USDS Joint Venture; Seattle (WA); On-site; USD 130,000 - 246,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

401(k) with company match

Parental leave

Wellbeing benefits

Paid holidays and PTO

Job summary

TikTok USDS Joint Venture LLC is seeking a Machine Learning Engineer to build and evolve a global-scale ML infrastructure for feeds, ads, and search ranking systems. You will work on scalable ML platforms and contribute to multi-tenant data pipelines and storage management.

You will design key ML components, mentor interns, and collaborate with cross-functional teams to push innovative AI infrastructure within a fully in-person work model.

Qualifications

• Bachelor's or Master's degree in Computer Science, Computer Engineering, or related fields or equivalent years of experience in a software engineering role
• Proficient in Go or Python in Linux environment with strong coding skills
• Experience with open source distributed scheduling/orchestration frameworks and ML system development
• Experience in developing and deploying large-scale systems

Responsibilities

• Design and implement a global-scale ML system for feeds, ads and search ranking models
• Improve usability and flexibility of ML infrastructure
• Enhance workflows for model training/serving, data pipelines, storage, and multi-tenancy resource management
• Design and develop key components of ML infrastructure and mentor interns

Skills

Go

Python

Linux

Distributed systems

Education

Bachelor's/Master's in CS/CE

Tools

Kubernetes

Flink

MapReduce

HDFS

Redis

S3

Celery

Job description

TikTok USDS Joint Venture LLC is seeking a Machine Learning Engineer to build and evolve a global-scale ML infrastructure for feeds, ads, and search ranking systems. You will work on scalable ML platforms and contribute to multi-tenant data pipelines and storage management.

You will design key ML components, mentor interns, and collaborate with cross-functional teams to push innovative AI infrastructure within a fully in-person work model.