ML Data Infrastructure Engineer

AppLovin

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Palo Alto, CA
Salary: $221,000–$331,000 / yr
Posted: 10 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $198k

This role $276k

$129k most similar roles pay here $353k

This role pays more than 94% of similar roles. Most pay $154,468–$241,750 — the shaded band above. At the midpoint, this role pays about $276k versus about $198k for comparable roles.

Based on 240 similar postings.

Employer

About AppLovin

AppLovin enables businesses to advertise profitably with marketing technologies that attract customers, increase revenue, and track ad performance.

AppLovin currently has 7 open roles on FindRole.

Listed pay typically runs $153,000–$200,000 across 7 roles with salary data.

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At a glance

TL;DR · ML Data Infrastructure Engineer

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As an ML Data Infrastructure Engineer on the ML Data Platform team, you will solve technical challenges by upgrading and implementing state-of-the-art software infrastructure within a high-performance, globally distributed ecosystem of services. You will design and build data processing infrastructure for model training and feature serving while optimizing for performance, reproducibility, and traceability. Your daily work involves collaborating with research teams to implement novel data processing architectures for emerging model paradigms and resolving performance bottlenecks across the training data pipeline from raw ingestion to feature delivery. You will also establish best practices and tooling for data infrastructure used across various machine learning teams. Required skills include experience with distributed computing frameworks like Apache Spark or Flink, strong software engineering fundamentals, systems design, and performance optimization to support high-throughput, fault-tolerant distributed systems.

Skills

MLOps Data Infrastructure Apache Spark Flink Distributed Systems Feature Stores model-serving Systems Design Performance Optimization

What you'll do

Design and build data processing infrastructure for model training and feature serving.
Optimize infrastructure for performance, reproducibility, and traceability.
Develop novel data processing architectures for emerging model and training paradigms with research teams.
Identify and resolve performance bottlenecks across the entire training data pipeline.
Manage the flow of data from raw ingestion to final feature delivery.
Establish best practices and tooling for data infrastructure used across multiple ML teams.
Build high-throughput, fault-tolerant distributed systems using frameworks like Apache Spark or Flink.

What we're looking for

Must have 1 - 3 years of experience and a minimum of a BS and/or MS in Computer Science.
Strong software engineering fundamentals with experience building high-throughput, fault-tolerant distributed systems.
Hands-on experience with distributed computing frameworks such as Apache Spark or Flink.
Solid grounding in data structures, systems design, and performance optimization.
Strong problem-solving skills and attention to detail.
Background in MLOps, Data Infrastructure, or ML Infrastructure (preferred).
Experience with ML training pipelines, feature stores, or model-serving systems (preferred).

ML Data Operations Engineer

Apple Inc

Santa Clara, CA 45 days ago $150,400–$225,300

Machine Learning Data Pipelines Dataset Management Technical Documentation Data Schema User Research Operations Behavioral Research Data Collection

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