Data Scientist, Agentic Systems (Remote)

About the role

The Data Science team is expanding and is looking for a Data Scientist to help build the next generation of agentic systems for cybersecurity. CrowdStrike's cybersecurity data is one-of-a-kind: we process nearly a trillion behavioral events per day. You'll work where Machine Learning, Big Data, and Cybersecurity converge — training models, building AI agents, and rigorously measuring whether they work — on data and problems you won't find anywhere else.

Responsibilities

• Work at the intersection of Artificial Intelligence and Threat Research
• Work closely with subject-matter experts in cybersecurity to understand analyst workflows and their security operations procedures
• Post-train LLMs and agents — supervised fine-tuning and reinforcement learning (RLHF/RLAIF, PPO/GRPO/DPO, reward modeling) — to automate analyst procedures and improve reliability on real security tasks
• Devise AI agents and combine them into increasingly complex workflows: planning and reasoning loops, tool and function calling, and retrieval and memory
• Research new approaches to agentic planning, and prototype state-of-the-art methods from the literature
• Establish objective criteria for benchmarking agentic systems — evals, LLM-as-judge pipelines, and trajectory-level metrics, with real statistical rigor
• Optimize prompts and inference to get the most out of every model
• Collaborate and coordinate across Engineering, Data Science, and Managed Services teams, and partner with engineers to take prototypes toward production
• Keep track of developments in the field of Artificial Intelligence and help identify, define, and prioritize areas for research

Requirements

• Excellent foundations in machine learning, probability, and statistics, with sound instincts for uncertainty, statistical skew/variance, and experimental design
• PhD-level depth of understanding in modern machine learning research — a doctorate itself is not required, but we expect equivalent mastery, including the ability to read, critique, implement, and improve upon current papers
• Experience training generative models, with a strong command of LLM training fundamentals (architecture, optimization, tokenization, data, and scaling behavior)
• Reinforcement learning / post-training as a core skill: RLHF/RLAIF, policy optimization (PPO/GRPO/DPO), reward modeling, and building RL environments for agents
• Experience building agentic systems: agent architectures (ReAct, planning, reflection), tool and function calling, and retrieval/memory/context management
• Experience with systematic prompt optimization, and with designing and building evals for LLM systems
• Strong, reproducible research engineering: clean Python and disciplined experiment tracking that your collaborators can build on
• Ability to work independently on ambiguous and complex objectives, and to communicate clearly within a large project team
• Proven experience utilizing AI technologies to enhance decision-making, streamline workflows and processes, improve efficiency and drive business outcomes

Qualifications

• Market leader in compensation and equity awards
• Comprehensive physical and mental wellness programs
• Competitive vacation and holidays for recharge
• Paid parental and adoption leaves
• Professional development opportunities for all employees regardless of level or role
• Employee Networks, geographic neighborhood groups, and volunteer opportunities to build connections
• Vibrant office culture with world class amenities
• Great Place to Work Certified™ across the globe

Skills

• Experience generating training data and environments — synthetic data, agent trajectories/rollouts, and task simulators
• Familiarity with inference-time scaling / test-time compute (search, self-consistency, verifier-guided decoding, long chain-of-thought)
• Experience with agent safety and guardrails: sandboxing, abuse/jailbreak resistance, and reliability for autonomous systems
• A knack for interpretability and failure analysis — diagnosing why a model or agent fails, not just that it does
• Noteable open-source contributions and excellent technical writing
• Passionate about cybersecurity, with a firm understanding of the problem space — or passionate about applying your machine-learning skillset to a new domain such as cybersecurity (a security background is a plus, not a requirement)
• An independent self-starter who likes to take ownership and seeks out new challenges, and is thirsty for knowledge — never hesitant to step outside your comfort zone to learn new technologies, algorithms, and concepts

Benefits

• Market leader in compensation and equity awards
• Comprehensive physical and mental wellness programs
• Competitive vacation and holidays for recharge
• Paid parental and adoption leaves
• Professional development opportunities for all employees regardless of level or role
• Employee Networks, geographic neighborhood groups, and volunteer opportunities to build connections
• Vibrant office culture with world class amenities
• Great Place to Work Certified™ across the globe