• Lead and manage teams of AI & Machine Learning Engineers • Own, execute, and iteratively evolve Zapier’s AI/ML strategy • Coach and guide teams in model development • Collaborate with cross-functional teams for integration of AI capabilities • Foster a culture of innovation • Communicate AI/ML strategy and results clearly • Design and run operating rhythms for AI/ML execution

• Lead the design, development, and deployment of production, multi-turn LLM-powered features, including summarization tools and clinician-facing conversational agents that support follow-up questions and reasoning over clinical context • Own backend services in Python that integrate LLM agents with Fullscript’s platform and support reliable production use • Help define technical direction for prompting, grounding, safety, and orchestration strategies used across clinical AI workflows • Establish and improve evaluation approaches for LLM outputs, including accuracy, hallucinations, edge cases, and overall feature quality • Shape engineering patterns for model-related workflows, including testing, CI/CD, observability, and version control • Partner with medical, product, and engineering teams to identify high-value opportunities for AI and turn them into practical, scalable product capabilities • Work cross-functionally with engineering, analytics, and medical SMEs to refine requirements and ensure data and system design support clinical use cases • Provide technical leadership across projects by creating clarity in ambiguous problem spaces, guiding tradeoff decisions, and raising the quality bar for the team • Stay current with the latest LLM research and emerging AI technologies, and help assess where they can be applied effectively at Fullscript

• Design and implement production-grade auto-labeling pipelines that generate 3D and 4D annotations (objects, trajectories, maps) from multi-modal robot data at scale. • Develop data-centric learning workflows that connect auto-label outputs, Serve’s dataset infrastructure, and continuous E2E model training and evaluation pipelines. • Lead initiatives in self-training, weak supervision, and simulation-to-real adaptation to reduce manual labeling dependency and accelerate model iteration cycles. • Collaborate cross-functionally with multiple autonomy teams(e.g ML-infra, mapping, simulation) to align labeling infrastructure with model training and evaluation workflows. • Stay ahead of emerging trends in E2E autonomy and data-centric ML, identifying opportunities to productionize state-of-the-art techniques. • Mentor and support ML engineers and interns in developing robust data-centric practices, from dataset curation and labeling feedback loops to model monitoring and continuous improvement.

• Deployment of ML pipelines in production • Improving and researching algorithms for Video2Haptic generation • Creating algorithms for video metadata extraction and action recognition • Improvement of the algorithms for high-resolution video matting • Improvement of the algorithms of content censuring • Research and development of algorithms for spatiotemporal ROI extraction in videos for short clips and trailer creation • Research and development into the field of volumetric video generation and streaming