Staff AI Engineer - EU

Salary: £65,000 - 100,000 per year

Requirements:

• Significant experience building and operating machine learning or AI systems in production, with technical leadership beyond individual projects.
• Track record of leading complex, cross-team engineering initiatives from ambiguous requirements to measurable production outcomes.
• Strong Python and software engineering skills, with the 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 PyTorch, LangChain, LangGraph, or similar frameworks.
• Strong experience designing and operating cloud systems using AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices.
• Familiarity with services such as AWS SageMaker or AWS Bedrock.
• Experience with event-driven processing, vector databases, and machine learning lifecycle tools such as Kafka and MLflow, or comparable technologies.
• Experience establishing observability and diagnosing production issues using tools such as Datadog or OpenSearch.
• Sound judgement balancing delivery speed, quality, reliability, scalability, security, and cost.
• Ability to influence technical decisions across teams and build alignment without relying on formal authority.
• Experience mentoring engineers and improving the effectiveness of teams.
• Additional relevant experience may include B2B SaaS, conversational AI, adaptive interviewing, automated analysis and summarisation, AI applications involving text, audio, or video, shared AI infrastructure, Airflow or Argo Workflows, SQL, Spark, Snowflake, AI security and privacy, responsible AI, prompt injection protection, data leakage prevention, or improving AI system latency, reliability, or cost at scale.

Responsibilities:

• Shape the technical direction of Research Flow with Product and Engineering leaders, translating product ambitions into technical direction and delivery priorities.
• Lead architectural decisions for AI-assisted study design, adaptive conversations, research synthesis, and the systems and data flows that support them.
• Identify technical constraints and dependencies early, balancing immediate delivery with long-term reliability, scalability, and maintainability.
• Own complex and ambiguous AI engineering problems from problem definition and exploration through production delivery.
• Design, build, and operate generative AI applications using large language models, RAG, vector search, tool use, and agentic systems; contribute through prototyping, production code, design reviews, and debugging.
• Lead cross-functional initiatives and build reusable services and APIs that help product teams deliver AI capabilities consistently.
• Make pragmatic build, buy, simplify, or stop decisions.
• Guide scalable machine learning services and workflows, including batch and real-time processing pipelines, retrieval and orchestration patterns, and structured and unstructured data systems.
• Improve experiment, model-version, registry, and deployment practices; resolve performance, reliability, and cost bottlenecks; and guide sustainable infrastructure and tooling choices.
• Define evaluation strategies and release criteria for generative AI, and guide automated benchmarks for accuracy, relevance, reliability, fairness, latency, and cost.
• Assess the usefulness of AI-generated follow-up questions and ensure summaries and insights are grounded in participant responses.
• Improve retrieval quality through chunking, embeddings, context selection, and reranking; connect offline evaluation with production monitoring and customer feedback.
• Build security, privacy, and safeguards against unexpected model behaviour into system design.
• Establish reusable standards and patterns for building, evaluating, deploying, and operating AI systems.
• Help engineers and technical leads navigate difficult decisions, make trade-offs explicit, grow ownership and judgement, and improve practices for testing, observability, security, incident response, and deployment.
• Build alignment around technical decisions through clear proposals, constructive discussion, and evidence; evaluate AI research and emerging tools for practical value.
• Partner with Product and Engineering to prioritise AI investments based on customer needs, technical feasibility, and business impact.
• Define measurable outcomes for AI initiatives, communicate technical concepts and trade-offs to technical and nontechnical partners, and clarify cross-team dependencies and sequencing.
• Help shape Typeforms broader AI direction using lessons from building and scaling Research Flow.

Technologies:

• AI
• Airflow
• AWS
• OpenSearch
• Cloud
• Datadog
• Docker
• FastAPI
• Flow
• HubSpot
• Support
• Kafka
• Kubernetes
• Machine Learning
• MLflow
• PyTorch
• Python
• RAG
• SQL
• Security
• Slack
• Snowflake
• Spark
• Terraform
• Zapier

More:

We are Typeform, a form builder helping more than 150,000 businesses collect data through forms, surveys, and quizzes. Our products are designed to be engaging and easy to complete, drive 500 million responses each year, and integrate with tools such as Slack, Zapier, and HubSpot. We are fully remote by design and can hire for this role in the UK, Ireland, Germany, Portugal, Spain, or the Netherlands. Our AI Engineering team builds the systems behind our AI products, including Research Flow, which combines quantitative research with qualitative insights and helps customers design studies, conduct AI-moderated conversations with adaptive follow-up questions, and turn responses into insights. The team works across machine learning, large language models, RAG, and agentic systems, owning the journey from experimentation through production, including application development, evaluation, infrastructure, deployment, observability, reliability, and performance. You will work with Product Managers, Software Engineers, Data Scientists, Data Engineers, and Analytics teams. This is a hands-on Staff AI Engineer individual contributor role with influence across teams, focused on the technical direction of Research Flow and AI systems across our products. We value diverse perspectives, respect, transparency, trust, collaboration, and customer focus. We are an equal-opportunity employer and welcome everyone; we stand against discrimination and harassment.

last updated 40 week of 2026