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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 Senior AI Engineer, you will be instrumental in building and evolving AI capabilities behind Research Flow and broader AI products. Your work will help customers understand the "why" behind responses and move from research questions to informed decisions faster. Your scope spans generative AI applications, enterprise RAG systems, agentic workflows, model evaluation, machine learning pipelines, and infrastructure to run them reliably at scale.
Key responsibilities include:
**Build and deliver AI products:** Design, build, and deploy generative AI capabilities across Typeform's products. Develop applications using large language models, RAG, vector search, and agentic systems. Build services and APIs for product teams to integrate AI capabilities. Turn prototypes into reliable production systems with clear performance measures. Explore new ways for customers to collect, understand, and act on information using AI.
**Build scalable AI systems:** Design and operate machine learning services and workflows using Python, Docker, Kubernetes, and AWS. Build reliable pipelines for batch and real-time processing using Kafka and Airflow. Design solutions using vector databases for retrieval, recommendations, personalization, and semantic search. Use MLflow to manage experiments, model versions, registries, and deployments. Improve reliability, performance, scalability, and cost efficiency of AI systems.
**Evaluate and improve AI quality:** Build automated evaluation pipelines for generative AI applications. Develop benchmarks measuring accuracy, relevance, reliability, fairness, latency, and cost. Evaluate retrieval strategies including chunking, embeddings, context selection, and reranking. Monitor AI systems in production and identify improvement opportunities. Create safeguards reducing unexpected behavior and protecting customer data.
**Shape AI engineering practice:** Establish reusable patterns and technical standards for building, evaluating, and releasing AI systems. Help teams make informed decisions about models, frameworks, infrastructure, performance, and cost. Apply strong engineering practices across testing, security, observability, version control, and deployment. Share technical knowledge and support other engineers' development. Keep up with relevant AI research, tools, and engineering practices.
**Collaborate across Typeform:** Partner with Product, Engineering, Data Science, Data Engineering, and Analytics teams. Work with Data Scientists to turn experiments and models into reliable production services. Communicate technical concepts, risks, and tradeoffs clearly to technical and nontechnical partners. Contribute to technical planning and shape AI direction across Typeform.
This is a hands-on engineering role with strong ownership. You will turn ideas and prototypes into production systems used by customers, with direct influence on their quality and performance. You will also help define technical standards for developing, evaluating, deploying, and monitoring AI across Typeform.
**Requirements:**
- At least four years of experience building and deploying machine learning or AI systems in production
- Strong Python and software engineering skills
- Experience building production services using Python frameworks such as FastAPI
- Practical experience developing generative AI applications using large language models, RAG, tool use, or agentic systems
- Experience with frameworks such as PyTorch, LangChain, LangGraph, or similar technologies
- Strong understanding of enterprise RAG systems, including chunking, embeddings, retrieval, reranking, evaluation, and monitoring
- Experience creating automated evaluations for generative AI applications
- Experience with AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices
- Experience using services such as AWS SageMaker or AWS Bedrock
- Experience with Kafka, vector databases, or other technologies used for real-time and high-dimensional data processing
- Experience managing machine learning workflows using MLflow
- Experience monitoring production systems with tools such as Datadog or OpenSearch
- Ability to balance quality, speed, reliability, scalability, and cost when making technical decisions
- Strong communication skills and experience collaborating with Product, Engineering, and Data teams
**Nice-to-have:**
- Experience working in a B2B SaaS product company
- Experience with orchestration tools such as Airflow or Argo Workflows
- Familiarity with SQL, Spark, Snowflake, or other data processing technologies
- Experience building systems combining structured data, unstructured data, and generative AI
- Experience with AI security, privacy, responsible AI, prompt injection protection, or data leakage prevention
- Experience improving latency and cost of AI systems operating at scale
