Senior ML Infrastructure Engineer – Ads Platform (Remote)

United States Digital Space LLC; United States; Remote; USD 150,000 - 190,000; Full time

Job summary

Instacar in the United States is seeking a Senior Software Engineer II in the Ads Quality ML infrastructure team to design, build, and deploy scalable training and serving systems for real-time ad recommendations.

You'll collaborate with product managers, data scientists, and MLEs to optimize throughput, latency, and cost while advancing online feature processing and foundation-model experimentation in a remote-friendly environment.

Qualifications

• Graduate degree in CS/AI/ML or equivalent.
• 3+ years of industry experience building ML infrastructure or solving real-world problems with large datasets.
• Deep expertise in ML infrastructure for training and serving in production.
• Strong programming in Python/Go and data manipulation with SQL/Spark/Pandas.
• Excellent communication and ability to collaborate with cross-functional teams.

Responsibilities

• Design, develop, and deploy ML infrastructure to tackle practical challenges in our marketplace.
• Optimize training and serving funnel bottlenecks for throughput, cost, and latency.
• Identify and fix instabilities in systems and procedures to improve availability.
• Build real-time feature processing pipelines for low-latency model input.
• Lead the infrastructure foundation for real-time sequential foundation models and training pipelines.
• Collaborate with product managers, data scientists, and MLEs to align solutions with business goals.
• Mentor engineers, set technical direction, and conduct code reviews.

Skills

Python

Go

SQL

Spark

Pandas

Torch

TensorFlow

XGBoost

Full stack ML infra

Analytics

Communication

curiosity

Education

Graduate degree in CS/AI/ML or equivalent

Tools

Kafka

Flink

Spark

Online feature store

Job description

Instacar in the United States is seeking a Senior Software Engineer II in the Ads Quality ML infrastructure team to design, build, and deploy scalable training and serving systems for real-time ad recommendations.

You'll collaborate with product managers, data scientists, and MLEs to optimize throughput, latency, and cost while advancing online feature processing and foundation-model experimentation in a remote-friendly environment.