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

• 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.
• 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.

Nice to Have

• 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).
• Experience scaling researcher productivity using Agentic AI.

  Why Arkose Labs?

  At Arkose Labs, our technology-driven approach enables us to make a substantial impact in the industry, supported by a robust customer base consisting of global enterprise giants such as Microsoft, Roblox, and more. We’re not just a company; we’re a collaborative ecosystem where you will actively partner with these influential brands, tackling the most demanding technical challenges to safeguard hundreds of millions of users across the globe.

  Why do top tech professionals choose Arkose Labs?

  Cutting-Edge Technology: Our high-efficacy solutions, backed by solid warranties, attract leading, global enterprise clients.Innovation and Excellence: We foster a culture that emphasizes technological innovation and the pursuit of excellence, ensuring a balanced and thriving work environment.Experienced Leadership: Guided by seasoned executives with deep tech expertise and a history of successful growth and equity events.Ideal Size: We’re structured to be agile and adaptable, large enough to provide stability, yet small enough to value your voice and ideas.

  Join us in shaping the future of technology. At Arkose Labs, you’re not just an employee; you’re part of a visionary team driving global change

  The most recognizable brands in the world select Arkose Labs, including Roblox, Microsoft, Adobe, Expedia, Snap and Meta.

  We value your unique contributions, perspectives, and experiences. Be part of a diverse and high-performing environment that prioritizes collaboration, excellence, and inclusion. We hire the best, focus on their professional development, and offer support for continuing education.

  We value:

  • People: first and foremost they are our most valuable resource. Our people are independent thinkers who make data driven decisions and take ownership and accountability in all the things they do.
  • Team Work. We demonstrate respect, trust, integrity, and communicate openly with a positive can do attitude and constructively challenge one another
  • Customer Focus. We empathize with our customers and obsess about solving their problems
  • Execution with precision, professionalism and urgency
  • Security. It’s the lens through which we implement our processes, procedures, and programs

  Benefits:

• Competitive salary + Equity
• Beautiful office space with many perks
• Robust benefits package
• Provident Fund
• Accident Insurance
• Hybrid office model with flexible working hours and work from home days to support personal well-being and mental health

Arkose Labs is an Equal Opportunity Employer that makes employment decisions without regard to race, color, religious creed, national origin, ancestry, sex, pregnancy, sexual orientation, gender, gender identity, gender expression, age, mental or physical disability, medical condition, military or veteran status, citizenship, marital status, genetic information, or any other characteristic protected by applicable law. In addition, Arkose Labs will provide reasonable accommodations for qualified individuals with disabilities.

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