This role involves leading data science work conceptualizing, designing, developing, and maintaining AI/ML models for disease progression and patient engagement programs. Meet with business partners to understand requirements and design solution(s) which work best for the problem Review AI/ML model ideas, engineering designs and offer ideas for new approaches Develop and evolve Data Science and Engineering roadmap for disease progression and patient engagement programs Raise the bar on excellence in execution in terms of quality, timeliness, and scalability Collaborate with counterparts in other geographical locations to ensure smooth execution of critical projects across the board Recommend applicable technical patterns and solutions that are aligned with architectural guiding principles and standards through a regular cadence of delivery and continuous improvement initiatives Collaborate closely with Product and Capability Architects on aligning inferred Solutions and Enterprise architecture to granular technical patterns and solutions Govern technical debt and promote balance

• Work with product, engineering, and design to identify customer and team pain points and build generative AI solutions using software engineering best practices • Work across the full stack - frontend, backend, and infrastructure - integrating AI capabilities directly into applications • Build intelligent search and retrieval systems that help users find relevant information quickly • Design effective interactions with foundation models (GPT-4, Claude, Gemini) to achieve reliable behaviors • Ship AI-powered features with the same reliability and performance standards as any other application components

• Lead development of Pathfinder Agent: our AI agent for building integrations on top of Membrane. • Responsible for product management (collecting and organizing requirements from internal stakeholders), architecture, and implementation in your area of responsibility. • Shape, plan and execute tasks and projects that improve Pathfinder Agent. • Design elegant and practical architecture in alignment with the rest of the product. • Work with internal (and occasionally external) stakeholders to inform your direction of work. • Ensure quality of your area of responsibility: documentation, test coverage, technical debt, etc.

• Design and deploy real-time voice and vision AI systems for classroom environments. • Evaluate tradeoffs between fine-tuning, RAG pipelines, prompt-based approaches, and tool-using agents. • Build scalable inference systems with strict latency and cost constraints. • Fine-tune and evaluate LLMs, ASR models, and vision/multimodal architectures. • Design robust evaluation frameworks (offline + online). • Implement safety guardrails and bias mitigation strategies. • Own the full ML lifecycle: data ingestion, training, validation, deployment, monitoring, and iteration. • Optimize infrastructure for GPU utilization, latency, and cost efficiency. • Partner with MLOps to build reliable, versioned AI pipelines. • Work closely with Product and Engineering to translate classroom needs into production-ready AI systems. • Clearly communicate tradeoffs between model complexity, performance, and cost. • Contribute to long-term AI roadmap and architecture decisions.