Director of AI Research & Data Operations

Cybersecurity & Trust and Safety | RLGym We are looking for a Director of AI Research & Data Operations to lead and scale RLGym's research and data generation efforts, delivering high-quality training and evaluation data to frontier AI models at scale. This role combines research leadership, data science, and large-scale operational management. You will build and lead multidisciplinary teams of researchers, data scientists, domain experts, and operations specialists, transforming complex research challenges into scalable, high-quality data production systems. The ideal candidate brings experience managing large research and data operations, a strong understanding of modern AI and LLM development, and the ability to deliver high-quality datasets that advance frontier model capabilities, with a particular focus on cybersecurity and trust & safety. What you'll be doing Lead and scale large AI research and data operations, managing multidisciplinary teams of researchers, data scientists, domain experts, and operational leaders. Own the end-to-end generation and delivery of high-quality training and evaluation datasets for frontier AI models, ensuring scalability, consistency, and research value. Define the research strategy, operational roadmap, and data generation priorities across cybersecurity, trust & safety, and emerging AI capabilities. Build and optimize large-scale data production pipelines, combining human expertise, automated workflows, synthetic data generation, and AI-assisted research. Establish rigorous data quality frameworks, including validation methodologies, quality assurance processes, benchmarking, and continuous improvement. Lead research initiatives focused on generating realistic, challenging, and diverse datasets for LLMs, GenAI applications, and agentic AI systems. Work closely with research, product, engineering, and data science teams to translate frontier model requirements into actionable research programs and scalable data deliverables. Develop scalable systems for expert-driven data generation, evaluation, annotation, and human feedback, including workflows supporting supervised fine-tuning and reinforcement learning. Define and track operational and research KPIs, balancing data quality, throughput, cost efficiency, and delivery timelines. Build partnerships with external researchers, domain experts, and research organizations to expand data generation capabilities and domain expertise. Drive innovation in data generation methodologies, evaluation benchmarks, and research infrastructure to continuously improve the quality and impact of RLGym's data. What we need to see Proven experience leading and scaling large research, data science, AI/ML, or data operations teams. Strong experience managing complex, high-volume data generation or research operations, with measurable quality and delivery outcomes. Deep understanding of AI/ML development, LLMs, GenAI, and the role of training and evaluation data in improving model performance. Experience designing and implementing data quality frameworks, evaluation methodologies, and scalable research workflows. Ability to translate complex research objectives into structured execution plans and deliver results at significant scale. Strong analytical and data-driven decision-making skills, with experience defining operational metrics and optimizing research processes. Experience managing multidisciplinary teams and collaborating with research scientists, engineers, and domain experts. Strong strategic leadership, operational execution, and cross-functional communication skills. Familiarity with cybersecurity, AI security, or trust & safety challenges and their relevance to frontier AI model development. 8+ years of relevant experience in AI/ML, data science, research leadership, large-scale data operations, or related technical fields. Ways to stand out Experience delivering large-scale training or evaluation datasets to leading frontier AI labs. Background in human feedback, RLHF, reinforcement learning environments, synthetic data generation, or expert-driven annotation programs. Experience building data generation platforms, automated evaluation pipelines, or large-scale AI research infrastructure. Familiarity with cybersecurity and trust & safety datasets, adversarial evaluations, or AI red-teaming methodologies. Experience developing data quality measurement systems, including automated grading, expert calibration, and statistical quality control. Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!