• Build and optimize data pipelines and backend services to process device and behavioral data in real time. • Develop and deploy ML models for fraud detection, ensuring they run reliably and efficiently in production. • Turn raw data into production-ready features that feed our fraud detection systems. • Collaborate with platform and backend engineers to integrate models seamlessly. • Maintain high standards of security, privacy, and compliance. • Champion best practices in testing, documentation, and observability.

• Build, optimize, and embed machine learning models for on-device inference within the QSIDS detection engine. • Optimize models for performance and resource constraints, minimizing memory footprint, latency, and compute on-device. • Collaborate closely with systems engineers to integrate models efficiently into a Go-based engine. • Take models all the way to production and own them once they're running, monitoring performance, detecting drift, and iterating to keep them reliable. • Collaborate with stakeholders to define milestones and ensure timelines align with development needs. • Participate in discussions on technological choices and decisions regarding product development. • Resolve anomalies and problems as soon as they arise. • Stay on the lookout for the latest ML and other technologies and best practices - but resist novelty chasing. • Help onboard and train colleagues as the team grows. • Effectively communicate technical concepts to both technical and non-technical stakeholders.

• Lead end-to-end machine learning initiatives focused on improving player engagement and retention, from initial concept through production deployment. • Build scalable, reusable machine learning pipelines with a focus on reliability, maintainability, and performance. • Design and manage CI/CD workflows for machine learning using tools like MLflow, Jenkins, and GitOps to enable automated and efficient model deployment. • Monitor model performance in production, implementing retraining strategies, drift detection, and continuous optimization. • Partner with cross-functional teams to translate business goals and user insights into high-impact machine learning solutions. • Mentor other engineers and help define best practices for machine learning system design, development, and deployment.

• Design and build systems that improve the efficiency of ML training and inference workloads. • Develop tooling that helps ML engineers debug, profile, optimize, and monitor model performance. • Improve GPU and general resource utilization through scheduling, resource management, caching, and workload optimization. • Partner with ML researchers and product teams to identify bottlenecks and drive performance improvements. • Build benchmarking frameworks and performance dashboards for training and serving systems. • Optimize distributed training infrastructure, data pipelines, and model serving architectures. • Lead cross-functional initiatives that improve the productivity of Reddit ML engineers. • Drive technical strategy for ML platform scalability, reliability, and cost efficiency.