Senior AI Engineer - US

Typeform·United States (Remote)Remotesenior

In short

→Construir y desplegar sistemas de IA generativa y RAG a gran escala para productos como Research Flow.
→Trabajar en el ciclo completo: prototipo a producción, con enfoque en calidad, rendimiento y observabilidad.
→Destacado: impacto directo en cómo los clientes entienden y actúan sobre respuestas, usando IA conversacional y agente.

Fluency in English required for collaboration with global teams.

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What they ask for

✓Experiencia comprobada en ingeniería de IA con modelos grandes y RAG.
✓Conocimiento sólido en Python, Docker, Kubernetes y AWS.
✓Experiencia con sistemas de búsqueda semántica y bases de datos vectoriales.
✓Capacidad para transformar prototipos en sistemas productivos escalables.
✓Conocimiento en evaluación automatizada de modelos de IA.
✓Colaboración en equipos multidisciplinarios (product, data science, eng).

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

PythonDockerKubernetesAWSKafkaAirflowMLflowRAGLarge Language ModelsVector Databases

Who should you write to at Typeform?

Your free dossier identifies the people who'd interview you — their background, what they value, and how to reach out so you stand out before applying.

Who we are Typeform 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. Typeform is fully remote by design. For this role, we can hire candidates based in the ET timezone in the US. About the team The 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. We 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. Through Research Flow, this includes helping customers design studies, run AI-moderated conversations with adaptive follow-up questions, and turn responses into useful insights. The team owns the journey from experimentation through to production. This includes AI application development, evaluation, infrastructure, deployment, observability, reliability, and performance. You 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. About the role As 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. Your work will help customers understand the “why” behind responses and move from research questions to informed decisions faster. 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 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. Things you will do Build and deliver AI products • Design, build, and deploy generative AI capabilities across Typeform’s products, with a key contribution to Research Flow. • Develop applications using large language models, RAG, vector search, and agentic systems. • Build services and APIs that allow product teams to integrate AI capabilities into customer experiences. • Turn prototypes into reliable production systems with clear measures of performance and quality. • Explore new ways for customers to collect, understand, and act on information using AI. Build scalable AI systems • Design and operate machine learning services and workflows using Python, Docker, Kubernetes, and AWS. • Build reliable pipelines for batch and real time processing using technologies such as Kafka and Airflow. • Design solutions using vector databases to support retrieval, recommendations, personalisation, and semantic search. • Use MLflow to manage experiments, model versions, registries, and deployments. • Improve the reliability, performance, scalability, and cost efficiency of our AI systems. Evaluate and improve AI quality • Build automated evalua

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