• Design and build the core architecture for AI systems spanning multiple product areas, ensuring coherence and reuse across different applications. • Set the technical bar for the org: evaluation frameworks, testing philosophy, CI/CD for model behavior, and shared infrastructure used across multiple teams. • Identify and resolve systemic engineering gaps before they become team-wide problems — proactively, not just in response to escalations. • Take direct ownership of the hardest, most ambiguous technical problems on the team — the ones without an established playbook — and build the solution end-to-end. • Build and ship conversational agents, deep learning models, and personalization systems that require novel technical approaches. • Improve AI/ML systems and services to drive behavior change and sustained metabolic health improvements across our member base. • Raise the technical level of the team through code review and design review — this is IC technical leadership, not people management. You will not have direct reports. • Partner with product, engineering leadership, and clinical/research teams to turn ambiguous goals into technical designs you then build. • Other duties as assigned.

• Provides, guides and uses professional concepts in developing resolution to critical issues. • Train and fine-tune models using LoRA and other parameter-efficient techniques • Design, train, and evaluate multimodal models from scratch • Apply reinforcement learning techniques to model development and optimization • Experience with audio/speech ML tasks such as speaker diarization and speaker identification • Deploy, debug, and maintain distributed systems in cloud environments • Optimize inference pipelines using tools and techniques such as TensorRT, quantization, and pruning • Monitor and maintain models in production, including handling data drift and system failures • Perform data gathering, cleaning, and curation to build high-quality training and evaluation datasets • Comfortable working with a gated, continuous deployment workflow that requires unit, component, and integration tests • Applies extensive knowledge of theories, practices and design matters. • Works on issues that impact program success or addresses future concepts and products. Implementation of solutions requires a longer-term view which impacts strategic goals and objectives spanning several functions. • Exercise a wide latitude in determining objectives and approaches to critical assignments. • Knowledge of media management strategies and distribution workflows • Work closely with Product and Operations to design and optimize workflows • Apply broad expertise and knowledge in highly specialized fields or several related disciplines • Contributes to development of company objectives across and principles to achieve goals • Work on significant and unique issues where analysis of situations or data requires an evaluation of intangibles

• Translate ambiguous product or business requirements into clear technical specifications, system designs, and validation plans. • Build, maintain, and scale AI-powered production systems and agentic applications. • Design workflows where LLMs, VLMs, tools, APIs, retrieval systems, and traditional microservices work together reliably. • Use AI-assisted development tools while maintaining code quality, security, performance, and long-term maintainability. • Prevent AI-generated code bloat, dead code, and unnecessary complexity. • Build observability, evaluation, testing, and monitoring systems for AI-generated outputs. • Optimize prompts, model selection, workflows, and inference cost. • Apply Responsible AI principles including safety, privacy, fairness, transparency, and accountability. • Work with cross-functional teams across product, engineering, security, data, and design. • Mentor engineers to move from implementation-heavy development to system orchestration and AI-first engineering practices.

• Build workflows that simplify automation through prompt-based inputs. • Integrate AI capabilities into actions like Email, SMS, and Code execution. • Implement AI-driven recipe and template suggestions based on user prompts. • Create tools for building custom AI agents to handle automation tasks. • Develop advanced AI features like Data Extraction, Text-to-Voice conversion, and Data Analysis. • Work with Retrieval-Augmented Generation (RAG) systems and knowledge bases to enable intelligent, context-aware workflows. • Optimize APIs to handle millions of hits monthly with low latency. • Mentor junior engineers and lead AI-driven projects.