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Salary: USD 170,000 - 210,000 / annual
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 play a central role in shaping 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:
**Shape Technical Direction**: Partner with Product and Engineering leaders to translate Research Flow's ambitions into clear technical direction and delivery priorities. Lead architectural decisions across AI-assisted study design, adaptive conversations, and research synthesis. Define how AI capabilities, data flows, and services work together as the product evolves.
**Lead Complex AI Engineering Work**: Take technical ownership of ambiguous problems from definition through production delivery. Design and build generative AI applications using LLMs, RAG, vector search, tool use, and agentic systems. Lead initiatives requiring coordination across Product, Engineering, Data Science, and Data Engineering. Build reusable services and APIs for consistent AI capability delivery.
**Build Scalable AI Foundations**: Guide architecture of machine learning services and workflows using Python, Docker, Kubernetes, and AWS. Design reliable pipelines for batch and real-time processing using Kafka and Airflow. Establish patterns for retrieval, vector search, model orchestration, and structured/unstructured data handling. Improve experiment management, model versioning, and deployments using MLflow. Identify and resolve performance, reliability, and cost bottlenecks.
**Set Standards for AI Quality**: Define evaluation strategies and release criteria for generative AI applications. Guide development of automated benchmarks covering accuracy, relevance, reliability, fairness, latency, and cost. Establish assessment methods for AI-generated follow-up questions and research summaries. Lead improvements to retrieval quality. Connect offline evaluation with production monitoring and customer feedback.
**Raise Engineering Standards**: Establish reusable patterns and technical standards for building, evaluating, deploying, and operating AI systems. Mentor engineers and support technical leads in growing ownership and judgment. Improve engineering practices across testing, observability, security, incident response, and deployment. Build alignment around technical decisions through clear proposals and evidence.
**Connect Technical Work to Customer Outcomes**: Work with Product and Engineering partners to prioritize AI investments. Help define measurable outcomes for AI initiatives. Communicate technical concepts, risks, and trade-offs clearly to technical and nontechnical partners. Contribute to cross-team planning.
**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 LLMs, 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
**Nice-to-have:**
- B2B SaaS product company experience
- Conversational AI, adaptive interviewing, or automated analysis/summarization systems
- Text, audio, or video in AI applications
- Evolving shared AI infrastructure or platforms for multiple product teams
- Orchestration tools such as Airflow or Argo Workflows
- SQL, Spark, Snowflake, or other data processing technologies
- AI security, privacy, responsible AI, prompt injection protection, or data leakage prevention
- Materially improving latency, reliability, or cost of AI systems at scale
