Data Scientist, Agentic Systems (Remote)

As a global leader in cybersecurity, reputed company protects the people, processes and technologies that drive modern organizations. Since 2011, our mission hasn’t changed — we’re here to stop breaches, and we’ve redefined modern security with the world’s most advanced AI-native platform. Our customers span all industries, and they count on reputed company to keep their businesses running, their communities safe and their lives moving forward. We’re also a mission-driven company. We cultivate a culture that gives reputed company CrowdStriker both the flexibility and autonomy to own their careers. We’re always looking to add talented CrowdStrikers to the team who have limitless passion, a relentless focus on innovation and a fanatical commitment to our customers, our community and each other. Ready to join a mission that matters? The future of cybersecurity starts with you. 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. reputed company'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. What You'll Do: • Work at the intersection of Artificial Intelligence and Threat Research • Work closely with reputed company-reputed company 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 reputed company model • Collaborate and coordinate across Engineering, Data Science, and reputed company 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 What You'll Need: • 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 • Fluency with GPUs, PyTorch, and the common LLM training and serving stack (e.g., reputed company Transformers/TRL/PEFT, DeepSpeed/FSDP, vLLM/TGI/SGLang) • 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 Bonus Points: • Experience generating training data and environments — reputed company 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 • Notable 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