Senior Machine Learning Engineer

CrowdStrike

Confirmed live today High trust
Remote

Quick summary

Work type
Remote
Location
CA
Salary
$140,000–$215,000 / yr
Employment
Full-time
Posted
22 days ago
Freshness
Confirmed live today

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $229k
This role $178k
$123k $294k
below market most similar roles pay here above market

This role pays less than 81% of similar roles. Most pay $202,412–$254,750 — the blue band above. At the midpoint, this role pays about $178k versus about $229k for comparable roles.

Based on 240 similar postings.

Employer

About CrowdStrike

CrowdStrike is a leading American cybersecurity technology firm, specializing in cloud-native endpoint protection, threat intelligence, and incident response.

CrowdStrike currently has 22 open roles on FindRole.

Listed pay typically runs $112,500–$167,500 across 22 roles with salary data.

Most-posted roles

View all roles at CrowdStrike

At a glance

TL;DR · Senior Machine Learning Engineer

The Sr. Machine Learning Engineer joins the AIDR Engineering Team to provide engineering support and solutions for a data science group focused on high-throughput, low-latency LLM inferencing. You will build and maintain data pipelines, contribute to the training and implementation of custom models, and support LLM post-training aligned to rigorous evaluations. The role involves designing for the AI age by allowing enterprise users to interact safely and securely with AI applications. Key technologies include Python, JVM technologies, Docker, Kubernetes, AWS, GCP, MaaS, Kafka, Cassandra, Spark, ElasticSearch, Terraform, Chef, and Ansible. You must possess experience in scaling inference across GPU clusters, distributed systems, and data engineering for advanced data science use cases while adhering to best practices like unit testing and continuous integration.

What does a Machine Learning Engineer earn in California?

Median $235750 from 200 postings across 34 companies.

See salary data

What you'll do

  • Provide engineering support for high-throughput, low-latency LLM inferencing and post-training.
  • Construct and maintain data pipelines aligned to rigorous evaluation standards.
  • Implement high-quality solutions for customer-facing applications at scale.
  • Identify potential vulnerabilities or gaps through in-depth analysis of systems.
  • Develop, test, deploy, and monitor changes autonomously from end to end.
  • Refine product architecture, knowledge models, performance, and reliability.
  • Mentor fellow engineers while actively absorbing knowledge across various technologies.

What we're looking for

  • Experience in data engineering and architecture supporting advanced data science use cases.
  • Deep understanding of LLM post-training methods and computational architectures.
  • Understanding of scalability and distributed systems, including sharding, partitioning, and concurrency.
  • Proven experience utilizing AI technologies to enhance decision-making and streamline workflows.
  • Proficiency in high-level coding languages such as Python or JVM technologies.
  • Experience scaling inference across GPUs or GPU clusters.
  • Knowledge of infrastructure tools like Docker, Kubernetes, AWS, GCP, Kafka, Cassandra, Spark, or Terraform.
  • Ability to work in a test-driven, collaborative environment following engineering best practices and peer code reviews.

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