Staff ML Infra Engineer — Real-Time Ranking

Coupang; Mountain View (WY); On-site; USD 174,000 - 299,000; Full time

Benefits offered by this job

Medical/Dental/Vision/Life insurance

401K Plan with Company Match

PTO 18-21 days/year

Job summary

Coupang is hiring a Staff Software Engineer, ML infra for the Search & Discovery group. You will design and build durable software that processes billions of records and serves complex ML models in real time to enhance product search and discovery.

You will lead data platform initiatives, collaborate with data scientists and engineers, and scale offline/online ML pipelines to support ranking and recommendation at scale.

Qualifications

• Bachelor's degree in computer science, electrical engineering, mathematics, statistics or closely related field.
• 5 years of professional experience in applied machine learning.
• Experience in machine learning, deep learning, and statistical modeling.

Responsibilities

• Proactively drive the end-to-end data platform and data quality roadmap for Search & Discovery ML efforts.
• Own, consolidate, and optimize the core data logging, processing, and ML model training pipelines.
• Develop and scale data infrastructure for batch and real-time data processing, monitoring, and debugging.
• Drive design and implementation of scalable ML-based ranking systems and online ML inference services.
• Build cross-functional partnerships with Data Scientists, Analysts, PMs, and Engineers to deliver on offline and online needs.
• Research, analyze, and select approaches; coach and mentor other engineers.

Skills

Python/Java

ML algorithms

Feature engineering

Education

Bachelor's degree in CS/EE/Math

Tools

Hadoop

Hive

Presto

Spark

Airflow

TensorFlow

PyTorch

Job description

Coupang is hiring a Staff Software Engineer, ML infra for the Search & Discovery group. You will design and build durable software that processes billions of records and serves complex ML models in real time to enhance product search and discovery.

You will lead data platform initiatives, collaborate with data scientists and engineers, and scale offline/online ML pipelines to support ranking and recommendation at scale.