Senior ML Engineer (Remote) — Scale LLMs for Security

CrowdStrike Inc.; Richmond (VA); Remote; USD 140,000 - 215,000; Full time

Benefits offered by this job

Market-leading compensation

Equity awards

Wellness programs

Generous vacation

Parental leave

Professional development

Employee networks

Inclusive culture

Job summary

CrowdStrike Inc. is seeking a Sr. Machine Learning Engineer to join the AIDR Engineering Team. You will help enable high-throughput, low-latency LLM inferencing and collaborate with data scientists on post-training processes and data engineering aligned to rigorous evaluations.

You will design for the AI age, contribute to data pipelines, model training, and scalable inference across GPU clusters, while mentoring others and improving product architecture and reliability.

Qualifications

• Experience in data engineering supporting advanced data science use cases.
• Deep understanding of LLM post-training methods and computational architectures.
• Understanding scalability and distributed systems (sharding, partitioning, concurrency).
• Proven ability to work collaboratively in a team environment.
• Strong engineering practices including testing, peer reviews, and resilient architecture.
• Experience applying AI technologies to improve workflows and business outcomes.

Responsibilities

• Innovate with state-of-the-art ML tech to accelerate data science.
• Support fast-moving research and development teams.
• Build and deploy solutions for customer-facing applications at scale.
• Analyze to identify vulnerabilities or gaps in systems.
• Design and maintain data pipelines and contribute to model training and deployment.

Skills

Data engineering

LLM post-training

Distributed systems

Team player

Testing & CI

AI workflows

Tools

Docker

Kubernetes

AWS

GCP

Kafka

Cassandra

Spark

Elasticsearch

Terraform

Job description

CrowdStrike Inc. is seeking a Sr. Machine Learning Engineer to join the AIDR Engineering Team. You will help enable high-throughput, low-latency LLM inferencing and collaborate with data scientists on post-training processes and data engineering aligned to rigorous evaluations.

You will design for the AI age, contribute to data pipelines, model training, and scalable inference across GPU clusters, while mentoring others and improving product architecture and reliability.