Machine Learning Director

About this role

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Skills you'll use

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
• 6+ 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.

Who can apply

• 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 monitoring.
• Proven people-management skills: hiring, coaching, and developing individual contributors.
• Excellent cross-functional communication; able to translate ML trade-offs for non-technical stakeholders.
• Experience with bot detection, device fingerprinting, behavioral biometrics, or real-time risk scoring.
• Experience with low-latency, high-throughput ML serving infrastructure.
• Familiarity with current ML research relevant to fraud/security (e.g., following or contributing to arXiv publications in adversarial ML, anomaly detection, or graph learning).

Perks & benefits

About Arkose Labs

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