{"schemaVersion":"jobsearcher.job.v1","id":"b5cc2a4e7e4b5ea2bdea866e","url":"https://jobsearcher.com/jobs/b5cc2a4e7e4b5ea2bdea866e","canonicalUrl":"https://jobsearcher.com/jobs/b5cc2a4e7e4b5ea2bdea866e","title":"Staff AI Engineer - US","description":"Who we are\n\nTypeform is a refreshingly different form builder. We help over 150,000 businesses collect the data they need with forms, surveys, and quizzes that people enjoy. Designed to look striking and feel effortless to fill out, Typeform drives 500 million responses every year—and integrates with essential tools like Slack, Zapier, and Hubspot.\n\nTypeform is fully remote by design. For this role, we can hire candidates based in the UK, Ireland, Germany, Portugal, Spain or the Netherlands.\n\nAbout the team\n\nThe AI Engineering team builds the systems and capabilities behind Typeform’s AI products, including Research Flow, our platform for combining quantitative research with deeper qualitative insights.\n\nWe use machine learning, large language models, RAG, and agentic systems to help customers collect, understand, and act on information in more conversational and personalised ways.\n\nThrough Research Flow, this includes helping customers design studies, run AI-moderated conversations with adaptive follow-up questions, and turn responses into useful insights.\n\nThe team owns the journey from experimentation through to production. This includes AI application development, evaluation, infrastructure, deployment, observability, reliability, and performance.\n\nYou will work closely with Product Managers, Software Engineers, Data Scientists, Data Engineers, and Analytics teams to turn promising AI ideas into secure, scalable, and dependable customer experiences.\n\nAbout the role\n\nAs 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.\n\nYou 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.\n\nYour 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.\n\nThis 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.\n\nThings you will do\n\nShape the technical direction of Research Flow\n\nPartner with Product and Engineering leaders to translate Research Flow’s product ambitions into a clear technical direction and delivery priorities.\nLead architectural decisions across AI-assisted study design, adaptive conversations, and research synthesis.\nIdentify the technical constraints and dependencies that matter most, and help teams address them early.\nDefine how AI capabilities, data flows, and services work together as the product evolves.\nBalance immediate delivery needs with longer-term reliability, scalability, and maintainability.\n\nLead and deliver complex AI engineering work\n\nTake technical ownership of ambiguous problems, from defining the problem and exploring approaches through to production delivery.\nDesign and build generative AI applications using large language models, RAG, vector search, tool use, and agentic systems.\nStay close to implementation through prototyping, production code, design reviews, and debugging.\nLead initiatives that require coordination across Product, Engineering, Data Science, and Data Engineering.\nBuild reusable services and APIs that help product teams deliver AI capabilities consistently.\nMake pragmatic decisions about when to build, buy, simplify, or stop an approach.\n\nBuild scalable AI foundations\n\nGuide the architecture of machine learning services and workflows using Python, Docker, Kubernetes, and AWS.\nDesign reliable pipelines for batch and real-time processing using technologies such as Kafka and Airflow.\nEstablish patterns for retrieval, vector search, model orchestration, and working with structured and unstructured data.\nImprove how we manage experiments, model versions, registries, and deployments using tools such as MLflow.\nIdentify and resolve performance, reliability, and cost bottlenecks across our AI systems.\nHelp teams choose infrastructure and tools that fit the problem and can be operated sustainably.\n\nSet the standard for AI quality\n\nDefine evaluation strategies and release criteria for generative AI applications, including Research Flow’s conversational and analytical capabilities.\nGuide the development of automated benchmarks covering accuracy, relevance, reliability, fairness, latency, and cost.\nEstablish ways to assess whether AI-generated follow-up questions are useful and whether summaries and insights are grounded in participants’ responses.\nLead improvements to retrieval quality, including chunking, embeddings, context selection, and reranking.\nConnect offline evaluation with production monitoring and customer feedback to guide improvements.\nBuild security, privacy, and safeguards against unexpected model behaviour into system design.\n\nRaise engineering standards across teams\n\nEstablish reusable patterns and technical standards for building, evaluating, deploying, and operating AI systems.\nHelp engineers reason through difficult technical decisions and make trade-offs explicit.\nMentor engineers and support other technical leads in growing their ownership and judgement.\nImprove engineering practices across testing, observability, security, incident response, and deployment.\nBuild alignment around technical decisions through clear proposals, constructive discussion, and evidence.\nEvaluate relevant AI research and emerging tools, and help teams adopt what delivers practical value.\n\nConnect technical work to customer outcomes\n\nWork with Product and Engineering partners to prioritise AI investments based on customer needs, technical feasibility, and business impact.\nHelp define measurable outcomes for AI initiatives and use them to assess whether an approach is working.\nCommunicate technical concepts, risks, and trade-offs clearly to technical and nontechnical partners.\nContribute to planning across teams, making dependencies and sequencing clear.\nHelp shape the broader direction of AI at Typeform through lessons learned from building and scaling Research Flow.\n\nWhat you bring\nSignificant experience building and operating machine learning or AI systems in production, with evidence of technical leadership beyond your own projects.\nA track record of leading complex engineering initiatives across teams, from ambiguous requirements to measurable production outcomes.\nStrong Python and software engineering skills, with the ability to contribute directly to production code.\nExperience designing production services and APIs using frameworks such as FastAPI.\nPractical experience building generative AI applications using large language models, RAG, tool use, or agentic systems.\nA strong understanding of enterprise RAG systems, including retrieval architecture, chunking, embeddings, reranking, evaluation, and monitoring.\nExperience defining evaluation approaches and using evidence to guide model, architecture, and release decisions.\nExperience with frameworks such as PyTorch, LangChain, LangGraph, or similar technologies.\nStrong experience designing and operating cloud systems using AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices.\nFamiliarity with services such as AWS SageMaker or AWS Bedrock.\nExperience with event-driven processing, vector databases, and machine learning lifecycle tools such as Kafka and MLflow, or comparable technologies.\nExperience establishing observability and diagnosing production issues using tools such as Datadog or OpenSearch.\nSound judgement when balancing delivery speed, quality, reliability, scalability, security, and cost.\nThe ability to influence technical decisions across teams and build alignment without relying on formal authority.\nExperience mentoring engineers and improving the effectiveness of the teams around you.\n\nExtra awesome\nExperience working in a B2B SaaS product company.\nExperience building conversational AI, adaptive interviewing, or automated analysis and summarisation systems.\nExperience working with text, audio, or video in AI applications.\nExperience evolving shared AI infrastructure or platforms used by multiple product teams.\nExperience with orchestration tools such as Airflow or Argo Workflows.\nFamiliarity with SQL, Spark, Snowflake, or other data processing technologies.\nExperience with AI security, privacy, responsible AI, prompt injection protection, or data leakage prevention.\nExperience materially improving the latency, reliability, or cost of AI systems operating at scale.\nNo one likes a guessing game — that’s why we're transparent about the salary range for this role. In addition to the base, we offer a 5-10% bonus depending on your level and performance. The range is broad because we tailor total compensation based on your location, experience, education, and skillset.\nWe also want to ensure equitable pay across the team and alignment with market data — but let us handle those details. We’re committed to clarity and honesty, so feel free to ask us anything along the way.\n\nPay range\n\n$170,000 - $210,000 USD\n\n*Typeform drives hundreds of millions of interactions each year, enabling conversational, human-centered experiences across the globe. We move as one team, empowering our collective efforts by valuing each individual’s unique perspective. This fosters strong bonds grounded in respect, transparency, and trust. We champion our diverse customer base by anticipating their needs and addressing their challenges with priority. Committed to excellence, we hold high expectations for ourselves and each other, continuously striving to deliver exceptional results.\n\nWe are proud to be an equal-opportunity employer. We celebrate diversity and stand firmly against discrimination and harassment of any kind—whether based on race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or expression, or veteran status. Everyone is welcome here.","company":"Typeform","rawCompany":"typeform","city":"Myrtle Point","state":"OR","isRemote":false,"isActive":false,"createdAt":"2026-09-28T12:05:02.318Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Staff AI Engineer - US","description":"Who we are\n\nTypeform is a refreshingly different form builder. We help over 150,000 businesses collect the data they need with forms, surveys, and quizzes that people enjoy. Designed to look striking and feel effortless to fill out, Typeform drives 500 million responses every year—and integrates with essential tools like Slack, Zapier, and Hubspot.\n\nTypeform is fully remote by design. For this role, we can hire candidates based in the UK, Ireland, Germany, Portugal, Spain or the Netherlands.\n\nAbout the team\n\nThe AI Engineering team builds the systems and capabilities behind Typeform’s AI products, including Research Flow, our platform for combining quantitative research with deeper qualitative insights.\n\nWe use machine learning, large language models, RAG, and agentic systems to help customers collect, understand, and act on information in more conversational and personalised ways.\n\nThrough Research Flow, this includes helping customers design studies, run AI-moderated conversations with adaptive follow-up questions, and turn responses into useful insights.\n\nThe team owns the journey from experimentation through to production. This includes AI application development, evaluation, infrastructure, deployment, observability, reliability, and performance.\n\nYou will work closely with Product Managers, Software Engineers, Data Scientists, Data Engineers, and Analytics teams to turn promising AI ideas into secure, scalable, and dependable customer experiences.\n\nAbout the role\n\nAs 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.\n\nYou 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.\n\nYour 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.\n\nThis 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.\n\nThings you will do\n\nShape the technical direction of Research Flow\n\nPartner with Product and Engineering leaders to translate Research Flow’s product ambitions into a clear technical direction and delivery priorities.\nLead architectural decisions across AI-assisted study design, adaptive conversations, and research synthesis.\nIdentify the technical constraints and dependencies that matter most, and help teams address them early.\nDefine how AI capabilities, data flows, and services work together as the product evolves.\nBalance immediate delivery needs with longer-term reliability, scalability, and maintainability.\n\nLead and deliver complex AI engineering work\n\nTake technical ownership of ambiguous problems, from defining the problem and exploring approaches through to production delivery.\nDesign and build generative AI applications using large language models, RAG, vector search, tool use, and agentic systems.\nStay close to implementation through prototyping, production code, design reviews, and debugging.\nLead initiatives that require coordination across Product, Engineering, Data Science, and Data Engineering.\nBuild reusable services and APIs that help product teams deliver AI capabilities consistently.\nMake pragmatic decisions about when to build, buy, simplify, or stop an approach.\n\nBuild scalable AI foundations\n\nGuide the architecture of machine learning services and workflows using Python, Docker, Kubernetes, and AWS.\nDesign reliable pipelines for batch and real-time processing using technologies such as Kafka and Airflow.\nEstablish patterns for retrieval, vector search, model orchestration, and working with structured and unstructured data.\nImprove how we manage experiments, model versions, registries, and deployments using tools such as MLflow.\nIdentify and resolve performance, reliability, and cost bottlenecks across our AI systems.\nHelp teams choose infrastructure and tools that fit the problem and can be operated sustainably.\n\nSet the standard for AI quality\n\nDefine evaluation strategies and release criteria for generative AI applications, including Research Flow’s conversational and analytical capabilities.\nGuide the development of automated benchmarks covering accuracy, relevance, reliability, fairness, latency, and cost.\nEstablish ways to assess whether AI-generated follow-up questions are useful and whether summaries and insights are grounded in participants’ responses.\nLead improvements to retrieval quality, including chunking, embeddings, context selection, and reranking.\nConnect offline evaluation with production monitoring and customer feedback to guide improvements.\nBuild security, privacy, and safeguards against unexpected model behaviour into system design.\n\nRaise engineering standards across teams\n\nEstablish reusable patterns and technical standards for building, evaluating, deploying, and operating AI systems.\nHelp engineers reason through difficult technical decisions and make trade-offs explicit.\nMentor engineers and support other technical leads in growing their ownership and judgement.\nImprove engineering practices across testing, observability, security, incident response, and deployment.\nBuild alignment around technical decisions through clear proposals, constructive discussion, and evidence.\nEvaluate relevant AI research and emerging tools, and help teams adopt what delivers practical value.\n\nConnect technical work to customer outcomes\n\nWork with Product and Engineering partners to prioritise AI investments based on customer needs, technical feasibility, and business impact.\nHelp define measurable outcomes for AI initiatives and use them to assess whether an approach is working.\nCommunicate technical concepts, risks, and trade-offs clearly to technical and nontechnical partners.\nContribute to planning across teams, making dependencies and sequencing clear.\nHelp shape the broader direction of AI at Typeform through lessons learned from building and scaling Research Flow.\n\nWhat you bring\nSignificant experience building and operating machine learning or AI systems in production, with evidence of technical leadership beyond your own projects.\nA track record of leading complex engineering initiatives across teams, from ambiguous requirements to measurable production outcomes.\nStrong Python and software engineering skills, with the ability to contribute directly to production code.\nExperience designing production services and APIs using frameworks such as FastAPI.\nPractical experience building generative AI applications using large language models, RAG, tool use, or agentic systems.\nA strong understanding of enterprise RAG systems, including retrieval architecture, chunking, embeddings, reranking, evaluation, and monitoring.\nExperience defining evaluation approaches and using evidence to guide model, architecture, and release decisions.\nExperience with frameworks such as PyTorch, LangChain, LangGraph, or similar technologies.\nStrong experience designing and operating cloud systems using AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices.\nFamiliarity with services such as AWS SageMaker or AWS Bedrock.\nExperience with event-driven processing, vector databases, and machine learning lifecycle tools such as Kafka and MLflow, or comparable technologies.\nExperience establishing observability and diagnosing production issues using tools such as Datadog or OpenSearch.\nSound judgement when balancing delivery speed, quality, reliability, scalability, security, and cost.\nThe ability to influence technical decisions across teams and build alignment without relying on formal authority.\nExperience mentoring engineers and improving the effectiveness of the teams around you.\n\nExtra awesome\nExperience working in a B2B SaaS product company.\nExperience building conversational AI, adaptive interviewing, or automated analysis and summarisation systems.\nExperience working with text, audio, or video in AI applications.\nExperience evolving shared AI infrastructure or platforms used by multiple product teams.\nExperience with orchestration tools such as Airflow or Argo Workflows.\nFamiliarity with SQL, Spark, Snowflake, or other data processing technologies.\nExperience with AI security, privacy, responsible AI, prompt injection protection, or data leakage prevention.\nExperience materially improving the latency, reliability, or cost of AI systems operating at scale.\nNo one likes a guessing game — that’s why we're transparent about the salary range for this role. In addition to the base, we offer a 5-10% bonus depending on your level and performance. The range is broad because we tailor total compensation based on your location, experience, education, and skillset.\nWe also want to ensure equitable pay across the team and alignment with market data — but let us handle those details. We’re committed to clarity and honesty, so feel free to ask us anything along the way.\n\nPay range\n\n$170,000 - $210,000 USD\n\n*Typeform drives hundreds of millions of interactions each year, enabling conversational, human-centered experiences across the globe. We move as one team, empowering our collective efforts by valuing each individual’s unique perspective. This fosters strong bonds grounded in respect, transparency, and trust. We champion our diverse customer base by anticipating their needs and addressing their challenges with priority. Committed to excellence, we hold high expectations for ourselves and each other, continuously striving to deliver exceptional results.\n\nWe are proud to be an equal-opportunity employer. We celebrate diversity and stand firmly against discrimination and harassment of any kind—whether based on race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or expression, or veteran status. Everyone is welcome here.","datePosted":"2026-09-28T12:05:02.318Z","dateModified":"2026-09-28T12:05:02.318Z","hiringOrganization":{"@type":"Organization","name":"Typeform","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Myrtle Point","addressRegion":"OR","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"b5cc2a4e7e4b5ea2bdea866e"},"url":"https://jobsearcher.com/jobs/b5cc2a4e7e4b5ea2bdea866e"}}