• Design, prototype and productionize scalable machine learning and optimization models • Play a critical in setting up the best practices in machine learning, setting direction of the machine learning platform • Develop frameworks, pipelines, libraries, utilities and tools that process massive data for ML tasks • Partner with data scientists to troubleshoot and optimize complex data pipelines • Work with product managers and business partners to gather requirements for machine learning models • Build model deployment platform that can simplify implementing new models • Build end-to-end reusable pipelines from data acquisition to model output delivery • Mentor and guide junior data scientists to deploy their models into production • Design & Build ML (engineering) solutions that unlock new ML modeling capabilities for BetterHelp

• Develop and maintain ML platforms and pipelines supporting autonomous, goal-driven AI agents. • Build systems for the full ML lifecycle, including agentic decision-making, task orchestration, and goal execution. • Integrate ML models with product logic and business workflows to operationalize AI capabilities. • Implement pipelines for experimentation, productionization, and continuous agentic learning. • Collaborate with data science and product teams to turn research outputs into production AI agents. • Design and optimize infrastructure for large-scale training, inference, and multi-agent coordination. • Implement observability and monitoring for ML pipelines, agent behaviors, and goal-driven execution. • Build systems for automated evaluation, drift detection, and retraining of AI models. • Ensure reliability, scalability, and operational excellence of ML services powering autonomous workflows. • Troubleshoot complex issues in ML pipelines, agentic systems, and distributed infrastructure. • Contribute to CI/CD and development workflows supporting ML lifecycle, agent orchestration, and model deployment. • Collaborate and share knowledge to improve implementation of agentic AI systems across teams.

• Architect and lead the development of scalable ML platforms that support autonomous, goal-driven AI agents. • Design systems that support the full ML lifecycle, including agentic decision-making, task orchestration, and automated goal execution. • Build frameworks for integrating models with product logic, business objectives, and operational workflows. • Lead the development of pipelines that enable experimentation, productionization, and continuous agentic learning. • Define architecture standards and engineering practices for agentic AI, goal alignment, and productized ML solutions. • Collaborate with data science and product teams to turn research outputs into production AI agents that drive real product impact. • Design infrastructure supporting large-scale training, inference, and multi-agent coordination workloads. • Strengthen observability and monitoring across pipelines, AI agents, and goal-driven behavior execution. • Implement systems for automated evaluation, goal alignment checks, drift detection, and retraining. • Improve reliability, scalability, and operational excellence of ML services powering autonomous workflows. • Lead troubleshooting of complex agentic system failures and distributed ML infrastructure issues. • Influence CI/CD and development workflows supporting ML lifecycle, agent orchestration, and automated deployment. • Mentor engineers to build expertise in agentic systems, AI-driven product logic, and autonomous workflows. • Collaborate with architects and senior engineers to shape long-term AI platform strategy and agentic product roadmaps.

• Develop large-scale, fault-tolerant multimodal agentic experiences that reach millions of customers • Design and develop multiple agent interaction modes, including Voice AI and Deep Research. • Pioneer robust evaluation frameworks, tracing systems, and safety guardrails to ensure our highly-visible AI agents remain trustworthy and responsible • Remain on the cutting edge of emerging agentic paradigms and convert them into tangible product innovation • Lead complex, multi-team initiatives, bridging the gap between applied research and highly scalable engineering • Distill complex research findings and AI system architectures into actionable insights for a diverse audience, including executives and cross-functional partner teams