Typeform - Staff AI Engineer - US

• Shape the technical direction of Research Flow • Partner with Product and Engineering leaders to translate Research Flow’s product ambitions into a clear technical direction and delivery priorities. • Lead architectural decisions across AI-assisted study design, adaptive conversations, and research synthesis. • Identify the technical constraints and dependencies that matter most, and help teams address them early. • Define how AI capabilities, data flows, and services work together as the product evolves. • Balance immediate delivery needs with longer-term reliability, scalability, and maintainability. • Lead and deliver complex AI engineering work • Take technical ownership of ambiguous problems, from defining the problem and exploring approaches through to production delivery. • Design and build generative AI applications using large language models, RAG, vector search, tool use, and agentic systems. • Stay close to implementation through prototyping, production code, design reviews, and debugging. • Lead initiatives that require coordination across Product, Engineering, Data Science, and Data Engineering. • Build reusable services and APIs that help product teams deliver AI capabilities consistently. • Make pragmatic decisions about when to build, buy, simplify, or stop an approach. • Build scalable AI foundations • Guide the architecture of machine learning services and workflows using Python, Docker, Kubernetes, and AWS. • Design reliable pipelines for batch and real-time processing using technologies such as Kafka and Airflow. • Establish patterns for retrieval, vector search, model orchestration, and working with structured and unstructured data. • Improve how we manage experiments, model versions, registries, and deployments using tools such as MLflow. • Identify and resolve performance, reliability, and cost bottlenecks across our AI systems. • Help teams choose infrastructure and tools that fit the problem and can be operated sustainably. • Set the standard for AI quality • Define evaluation strategies and release criteria for generative AI applications, including Research Flow’s conversational and analytical capabilities. • Guide the development of automated benchmarks covering accuracy, relevance, reliability, fairness, latency, and cost. • Establish ways to assess whether AI-generated follow-up questions are useful and whether summaries and insights are grounded in participants’ responses. • Lead improvements to retrieval quality, including chunking, embeddings, context selection, and reranking. • Connect offline evaluation with production monitoring and customer feedback to guide improvements. • Build security, privacy, and safeguards against unexpected model behaviour into system design. • Raise engineering standards across teams • Establish reusable patterns and technical standards for building, evaluating, deploying, and operating AI systems. • Help engineers reason through difficult technical decisions and make trade-offs explicit. • Mentor engineers and support other technical leads in growing their ownership and judgement. • Improve engineering practices across testing, observability, security, incident response, and deployment. • Build alignment around technical decisions through clear proposals, constructive discussion, and evidence. • Evaluate relevant AI research and emerging tools, and help teams adopt what delivers practical value. • Connect technical work to customer outcomes • Work with Product and Engineering partners to prioritise AI investments based on customer needs, technical feasibility, and business impact. • Help define measurable outcomes for AI initiatives and use them to assess whether an approach is working. • Communicate technical concepts, risks, and trade-offs clearly to technical and nontechnical partners. • Contribute to planning across teams, making dependencies and sequencing clear. • Help shape the broader direction of AI at Typeform through lessons learned from building and scaling Research Flow.