[Hiring] Staff AI Engineer @Typeform

Role Description

As a Staff AI Engineer at Typeform, you will play a central role in shaping the technical direction of Research Flow and the AI systems that support our broader products. You will take ownership of complex engineering challenges that span teams, from defining architecture and testing new approaches to delivering and operating production systems. A key part of your role will be connecting individual AI capabilities into a dependable customer experience.

Your scope will span generative AI applications, enterprise RAG systems, agentic workflows, model evaluation, machine learning pipelines, and the infrastructure required to run them reliably at scale. This is a hands-on individual contributor role with influence beyond a single project. You will lead through technical judgement, delivery, and collaboration, helping teams make sound decisions and building foundations that other engineers can use.

Things you will do

• 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.

Qualifications

• Significant experience building and operating machine learning or AI systems in production, with evidence of technical leadership beyond your own projects.
• A track record of leading complex engineering initiatives across teams, 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.
• A 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 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 when balancing delivery speed, quality, reliability, scalability, security, and cost.
• The ability to influence technical decisions across teams and build alignment without relying on formal authority.
• Experience mentoring engineers and improving the effectiveness of the teams around you.

Extra awesome

• Experience working in a B2B SaaS product company.
• Experience building conversational AI, adaptive interviewing, or automated analysis and summarisation systems.
• Experience working 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 the latency, reliability, or cost of AI systems operating at scale.

Pay range

$170,000 — $210,000 USD