{"id":1088451,"url":"https://alion.io/job/typeform-senior-ai-engineer-us","title":"Senior AI Engineer - US","company":{"id":47899,"name":"Typeform","domain":"typeform.com","url":"https://alion.io/company/typeform","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":{"label":"ET","utc_offset_min":-5,"utc_offset_max":-5},"hiring_geo_confidence":"explicit","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":133000,"max_usd":240000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":314},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Apache Kafka","optional":false},{"name":"Argo Workflows","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"CI/CD","optional":false},{"name":"Datadog","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"OpenSearch","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Tool Use","optional":false},{"name":"Slack","optional":true},{"name":"Zapier","optional":true}],"status":"live","first_seen_at":"2026-09-21T09:59:54Z","employer_posted_date":"2026-09-21","last_verified_at":"2026-10-17T13:08:06Z","board_verified":true,"closed_at":null,"days_open":26,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":26},"description":"Who we are\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.\nTypeform is fully remote by design. For this role, we can hire candidates based in the ET timezone in the US.\nAbout the team\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.\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.\nThrough Research Flow, this includes helping customers design studies, run AI-moderated conversations with adaptive follow-up questions, and turn responses into useful insights.\nThe team owns the journey from experimentation through to production. This includes AI application development, evaluation, infrastructure, deployment, observability, reliability, and performance.\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.\nAbout the role\nAs a Senior AI Engineer at Typeform, you will be instrumental in building and evolving the AI capabilities behind Research Flow, alongside our broader AI products.\nYour work will help customers understand the “why” behind responses and move from research questions to informed decisions faster.\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.\nThis is a hands-on engineering role with strong ownership. You will turn ideas and prototypes into production systems used by our customers, with direct influence on their quality and performance. You will also help define the technical standards for developing, evaluating, deploying, and monitoring AI across Typeform.\nThings you will do\nBuild and deliver AI products\nDesign, build, and deploy generative AI capabilities across Typeform’s products, with a key contribution to Research Flow.\nDevelop applications using large language models, RAG, vector search, and agentic systems.\nBuild services and APIs that allow product teams to integrate AI capabilities into customer experiences.\nTurn prototypes into reliable production systems with clear measures of performance and quality.\nExplore new ways for customers to collect, understand, and act on information using AI.\nBuild scalable AI systems\nDesign and operate machine learning services and workflows using Python, Docker, Kubernetes, and AWS.\nBuild reliable pipelines for batch and real time processing using technologies such as Kafka and Airflow.\nDesign solutions using vector databases to support retrieval, recommendations, personalisation, and semantic search.\nUse MLflow to manage experiments, model versions, registries, and deployments.\nImprove the reliability, performance, scalability, and cost efficiency of our AI systems.\nEvaluate and improve AI quality\nBuild automated evaluation pipelines for generative AI applications, including the conversational and analytical capabilities behind Research Flow.\nDevelop benchmarks that measure accuracy, relevance, reliability, fairness, latency, and cost.\nEvaluate retrieval strategies, including chunking, embeddings, context selection, and reranking.\nMonitor AI systems in production and identify opportunities to improve their quality and performance.\nCreate safeguards that reduce unexpected behaviour and protect customer data.\nShape our AI engineering practice\nEstablish reusable patterns and technical standards for building, evaluating, and releasing AI systems.\nHelp teams make informed decisions about models, frameworks, infrastructure, performance, and cost.\nApply strong engineering practices across testing, security, observability, version control, and deployment.\nShare technical knowledge and support the development of other engineers.\nKeep up with relevant AI research, tools, and engineering practices, applying what is useful to Typeform.\nCollaborate across Typeform\nPartner with Product, Engineering, Data Science, Data Engineering, and Analytics teams to connect AI investments with customer and business needs.\nWork with Data Scientists to turn experiments and models into reliable production services.\nCommunicate technical concepts, risks, and tradeoffs clearly to technical and nontechnical partners.\nContribute to technical planning and help shape the direction of AI across Typeform.\nWhat you bring\nAt least four years of experience building and deploying machine learning or AI systems in production.\nStrong Python and software engineering skills.\nExperience building production services using Python frameworks such as FastAPI.\nPractical experience developing generative AI applications using large language models, RAG, tool use, or agentic systems.\nExperience with frameworks such as PyTorch, LangChain, LangGraph, or similar technologies.\nA strong understanding of enterprise RAG systems, including chunking, embeddings, retrieval, reranking, evaluation, and monitoring.\nExperience creating automated evaluations for generative AI applications.\nExperience with AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices.\nExperience using services such as AWS SageMaker or AWS Bedrock.\nExperience with Kafka, vector databases, or other technologies used for real time and high dimensional data processing.\nExperience managing machine learning workflows using MLflow.\nExperience monitoring production systems with tools such as Datadog or OpenSearch.\nThe ability to balance quality, speed, reliability, scalability, and cost when making technical decisions.\nStrong communication skills and experience collaborating with Product, Engineering, and Data teams.\nExtra awesome\nExperience working in a B2B SaaS product company.\nExperience with orchestration tools such as Airflow or Argo Workflows.\nFamiliarity with SQL, Spark, Snowflake, or other data processing technologies.\nExperience building systems that combine structured data, unstructured data, and generative AI.\nExperience with AI security, privacy, responsible AI, prompt injection protection, or data leakage prevention.\nExperience improving the latency and cost of AI systems operating at scale.\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.\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. 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