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Typeform is a form builder used by over 150,000 businesses to collect data through forms, surveys, and quizzes. The company processes 500 million responses annually and integrates with tools like Slack, Zapier, and HubSpot.
The AI Engineering team builds systems and capabilities behind Typeform's AI products, including Research Flow—a platform combining quantitative research with qualitative insights. The team uses machine learning, large language models, RAG, and agentic systems to help customers collect, understand, and act on information in conversational and personalized ways.
As a Staff AI Engineer, you will shape the technical direction of Research Flow and AI systems across the broader product. This is a hands-on individual contributor role with influence beyond a single project. You will lead through technical judgment, delivery, and collaboration—helping teams make sound decisions and building foundations other engineers can use.
Key responsibilities include:
- Shaping the technical direction of Research Flow by partnering with Product and Engineering leaders, leading architectural decisions, and defining how AI capabilities and data flows work together.
- Leading and delivering complex AI engineering work: taking ownership of ambiguous problems from definition through production delivery, designing generative AI applications using LLMs, RAG, vector search, and agentic systems, and building reusable services and APIs.
- Building scalable AI foundations: guiding architecture of ML services using Python, Docker, Kubernetes, and AWS; designing reliable pipelines for batch and real-time processing; establishing patterns for retrieval, vector search, and model orchestration.
- Setting standards for AI quality: defining evaluation strategies and release criteria for generative AI applications, developing automated benchmarks, and connecting offline evaluation with production monitoring.
- Raising engineering standards across teams: establishing reusable patterns and technical standards, mentoring engineers, and improving practices across testing, observability, security, and deployment.
- Connecting technical work to customer outcomes: prioritizing AI investments based on customer needs and business impact, defining measurable outcomes, and communicating technical concepts to technical and nontechnical partners.
You will work closely with Product Managers, Software Engineers, Data Scientists, Data Engineers, and Analytics teams. The team owns the journey from experimentation through to production, including AI application development, evaluation, infrastructure, deployment, observability, reliability, and performance.
REQUIREMENTS:
- Significant experience building and operating machine learning or AI systems in production, with evidence of technical leadership beyond your own projects.
- Track record of leading complex engineering initiatives across teams, from ambiguous requirements to measurable production outcomes.
- Strong Python and software engineering skills, with ability to contribute directly to production code.
- Experience designing production services and APIs using frameworks such as FastAPI.
- Practical experience building generative AI applications using large language models, RAG, tool use, or agentic systems.
- Strong understanding of enterprise RAG systems, including retrieval architecture, chunking, embeddings, reranking, evaluation, and monitoring.
- Experience defining evaluation approaches and using evidence to guide model, architecture, and release decisions.
- Experience with frameworks such as PyTorch, LangChain, LangGraph, or similar technologies.
- Strong experience designing and operating cloud systems using AWS, Docker, Kubernetes, Terraform, and CI/CD practices.
- Familiarity with AWS SageMaker or AWS Bedrock.
- Experience with event-driven processing, vector databases, and ML lifecycle tools such as Kafka and MLflow.
- Experience establishing observability and diagnosing production issues using tools such as Datadog or OpenSearch.
- Sound judgment balancing delivery speed, quality, reliability, scalability, security, and cost.
- Ability to influence technical decisions across teams and build alignment without formal authority.
- Experience mentoring engineers and improving team effectiveness.
PREFERRED:
- Experience in B2B SaaS product companies.
- Experience building conversational AI, adaptive interviewing, or automated analysis and summarization systems.
- Experience with text, audio, or video in AI applications.
- Experience evolving shared AI infrastructure or platforms used by multiple product teams.
- Experience with orchestration tools such as Airflow or Argo Workflows.
- Familiarity with SQL, Spark, Snowflake, or other data processing technologies.
- Experience with AI security, privacy, responsible AI, prompt injection protection, or data leakage prevention.
- Experience materially improving latency, reliability, or cost of AI systems at scale.
