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

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., Hugging Face 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

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

Data Scientist

Key skills