AI Research Scientist, Agentic Systems (Remote)

CrowdStrike•Austin, TX

20h•$120,000 - $180,000•Remote

About The Position

CrowdStrike’s Data Science team is seeking a Data Scientist to build next-generation agentic systems for cybersecurity. The role works at the intersection of machine learning, big data, and cybersecurity, using CrowdStrike’s large-scale behavioral data to train models and build and rigorously evaluate AI agents for real security tasks. The position partners with cybersecurity subject-matter experts to understand analyst workflows and security operations procedures, and collaborates with Engineering, Data Science, and Managed Services teams to move research prototypes toward production. The role focuses on post-training large language models and agents, developing agentic workflows and planning methods, and establishing statistically rigorous benchmarks and evaluation pipelines. It also involves optimizing prompts and inference, tracking advances in AI, and identifying and prioritizing research opportunities. A doctorate is not required, but the position expects equivalent, PhD-level mastery of modern machine learning research.

Requirements

Excellent foundations in machine learning, probability, and statistics, including sound judgment about uncertainty, statistical skew and variance, and experimental design.
PhD-level understanding of modern machine-learning research or equivalent mastery, including the ability to read, critique, implement, and improve upon current papers; a doctorate is not required.
Experience training generative models and strong command of LLM training fundamentals, including architecture, optimization, tokenization, data, and scaling behavior.
Reinforcement learning and post-training expertise, including RLHF/RLAIF, PPO/GRPO/DPO policy optimization, reward modeling, and building RL environments for agents.
Experience building agentic systems, including agent architectures, planning, reflection, tool and function calling, and retrieval, memory, and context management.
Experience with systematic prompt optimization and designing and building evaluations for LLM systems.
Fluency with GPUs, PyTorch, and common LLM training and serving tools such as Hugging Face Transformers/TRL/PEFT, DeepSpeed/FSDP, and vLLM/TGI/SGLang.
Strong, reproducible research engineering skills, including clean Python and disciplined experiment tracking.
Ability to work independently on ambiguous, complex objectives and communicate clearly within a large project team.
Proven experience using AI technologies to enhance decision-making, streamline workflows and processes, improve efficiency, and drive business outcomes.

Nice To Haves

Experience generating training data and environments, including synthetic data, agent trajectories or rollouts, and task simulators.
Familiarity with inference-time scaling or test-time compute, including search, self-consistency, verifier-guided decoding, and long chain-of-thought.
Experience with agent safety and guardrails, including sandboxing, abuse and jailbreak resistance, and reliability for autonomous systems.
Skills in interpretability and failure analysis, including diagnosing why a model or agent fails.
Notable open-source contributions and excellent technical writing.
Passion for cybersecurity and a strong understanding of the problem space, or interest in applying machine-learning skills to cybersecurity; security experience is a plus, not a requirement.
An independent, self-starting approach, ownership, willingness to take on new challenges, and eagerness to learn new technologies, algorithms, and concepts.

Responsibilities

Work at the intersection of artificial intelligence and threat research.
Work with cybersecurity subject-matter experts to understand analyst workflows and security operations procedures.
Post-train LLMs and agents using supervised fine-tuning and reinforcement-learning approaches to automate analyst procedures and improve reliability on security tasks.
Design AI agents and combine them into workflows using planning and reasoning loops, tool and function calling, retrieval, and memory.
Research agentic planning approaches and prototype state-of-the-art methods from the literature.
Establish objective benchmarks for agentic systems, including evaluations, LLM-as-judge pipelines, and trajectory-level metrics with statistical rigor.
Optimize prompts and inference to improve model performance.
Collaborate across Engineering, Data Science, and Managed Services, and partner with engineers to move prototypes toward production.
Track AI developments and help identify, define, and prioritize research areas.

Benefits

Market-leading compensation and equity awards.
Comprehensive physical and mental wellness programs.
Competitive vacation and holidays.
Paid parental and adoption leaves.
Professional development opportunities for all employees, regardless of level or role.
Employee Networks, geographic neighborhood groups, and volunteer opportunities.
Office culture with world-class amenities.
Bonuses and equity grants.
Health insurance.
401(k).
Paid time off.