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TL;DR
Sr. Machine Learning Engineer (LLM/Data Engineering): Building high-throughput, low-latency LLM inference solutions, data pipelines, and custom models for secure enterprise AI applications with an accent on post-training, scalable architectures, and distributed systems. Focus on scaling GPU inference, designing rigorous evaluations, identifying vulnerabilities, and delivering tested, monitored software for customer-facing applications.
Location: USA - Remote
Salary: $140,000–$215,000 per year, with eligibility for bonuses and equity grants.
Company
CrowdStrike is a cybersecurity company developing an AI-native platform that protects organizations and processes nearly 3 trillion events per day.
What you will do
- Engineer high-throughput, low-latency LLM inference solutions for enterprise AI applications.
- Support LLM post-training, rigorous evaluations, data engineering, and custom model implementation.
- Construct and maintain data pipelines and scalable customer-facing applications.
- Analyze potential vulnerabilities and gaps while improving product architecture, performance, and reliability.
- Develop, test, deploy, and monitor changes with strong coding, logging, metrics, and continuous integration practices.
- Collaborate with data scientists and engineering teams, contribute to technical discussions, and mentor other engineers.
Requirements
- Prior 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.
- Experience with engineering best practices, test-driven development, peer code reviews, resilient architecture, and continuous integration.
- Ability to deliver high-quality, unit-tested software and collaborate in an iterative environment.
- Experience using AI technologies to improve decision-making, workflows, efficiency, and business outcomes.
Nice to have
- Applied experience with scalable architectures for LLM post-training or fine-tuning.
- Experience in cybersecurity or intelligence.
- Experience scaling inference across GPUs or GPU clusters.
Culture & Benefits
- Full-time employment with health insurance, 401(k), paid time off, bonuses, and equity grants.
- Competitive vacation and holidays, plus paid parental and adoption leave.
- Physical and mental wellness programs.
- Professional development opportunities for employees at all levels.
- Employee networks, geographic neighborhood groups, and volunteer opportunities.
- Collaborative, flexible, and autonomous work environment with an emphasis on responsible AI adoption.