Senior Manager, Machine Learning

Arkose Labs is on a mission to create an online environment where all consumers are protected from spam and abuse. As a Fast Company 2025 Best Workplace for Innovators, we provide a proactive fraud deterrence platform, Arkose Titan, designed to neutralize modern attacks powered by Agentic AI and LLMs. By combining proprietary intelligence with dynamic friction, we undermine attacker ROI to protect global giants like Microsoft, Meta, and Roblox. Headquartered in San Mateo, CA, we maintain a global presence across APAC, Central and South America, and EMEA. The Role We’re hiring a Machine Learning Research Manager to lead our ML team and set the technical direction for how machine learning is applied across Arkose’s fraud detection and risk-scoring products. This is a builder-manager role: you’ll grow and coach a small, high-leverage team while staying hands-on with model design, architecture decisions, and code review. You’ll own the connection between fraud outcomes — detection accuracy, false positive rates — and the ML systems that drive them. This role will report to the SVP of Product. What You’ll Do Define and drive Arkose’s Data Science and Machine Learning roadmap across Arkose Titan by aligning research with product priorities. Lead development and delivery of ML models and capabilities that power real-time fraud and risk decisioning at Arkose’s scale, in collaboration with detection engineering to turn emerging fraud patterns into features and models. Establish modern MLOps practices — model governance, experimentation frameworks, and end-to-end model lifecycle management — and own model performance in production, including precision/recall tradeoffs, drift monitoring, retraining cadence, and latency/cost constraints. Drive applied research in graph machine learning, behavioral analytics, and intelligent fraud detection systems — resulting in patents, publications, and product innovation. Manage, coach, and grow a team of ML researchers, including performance management, career development, and future hiring. Stay hands-on: contribute to model design, feature engineering, architecture reviews, and code/PR review alongside the team. Translate fraud and business metrics into measurable ML objectives, and communicate roadmap and tradeoffs to leadership and cross-functional stakeholders. What We’re Looking For 10+ years building and deploying ML models in production, including 2+ years directly managing ML engineers or data scientists. A track record of shipping ML systems that moved a real business metric, ideally in fraud, trust & safety, cybersecurity, or another adversarial, imbalanced-data domain. Strong technical depth — able to evaluate modeling and architecture choices (classification, anomaly detection, graph-based methods, sequence models) rather than managing from the roadmap alone. Experience across the full ML lifecycle: data pipelines, feature engineering, training, evaluation, deployment, and…