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About This Role
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 evaluation pipelines for generative AI applications, including the conversational and analytical capabilities behind Research Flow.
- Develop benchmarks that measure accuracy, relevance, reliability, fairness, latency, and cost.
- Evaluate retrieval strategies, including chunking, embeddings, context selection, and reranking.
- Monitor AI systems in production and identify opportunities to improve their quality and performance.
- Create safeguards that reduce unexpected behaviour and protect customer data.
Shape our AI engineering practice
- Establish reusable patterns and technical standards for building, evaluating, and releasing AI systems.
- Help teams make informed decisions about models, frameworks, infrastructure, performance, and cost.
- Apply strong engineering practices across testing, security, observability, version control, and deployment.
- Share technical knowledge and support the development of other engineers.
- Keep up with relevant AI research, tools, and engineering practices, applying what is useful to Typeform.
Collaborate across Typeform
- Partner with Product, Engineering, Data Science, Data Engineering, and Analytics teams to connect AI investments with customer and business needs.
- Work with Data Scientists to turn experiments and models into reliable production services.
- Communicate technical concepts, risks, and tradeoffs clearly to technical and nontechnical partners.
- Contribute to technical planning and help shape the direction of AI across Typeform.
What you bring
- At least four years of experience building and deploying machine learning or AI systems in production.
- Strong Python and software engineering skills.
- Experience building production services using Python frameworks such as FastAPI.
- Practical experience developing generative AI applications using large language models, RAG, tool use, or agentic systems.
- Experience with frameworks such as PyTorch, LangChain, LangGraph, or similar technologies.
- A strong understanding of enterprise RAG systems, including chunking, embeddings, retrieval, reranking, evaluation, and monitoring.
- Experience creating automated evaluations for generative AI applications.
- Experience with AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices.
- Experience using services such as AWS SageMaker or AWS Bedrock.
- Experience with Kafka, vector databases, or other technologies used for real time and high dimensional data processing.
- Experience managing machine learning workflows using MLflow.
- Experience monitoring production systems with tools such as Datadog or OpenSearch.
- The ability to balance quality, speed, reliability, scalability, and cost when making technical decisions.
- Strong communication skills and experience collaborating with Product, Engineering, and Data teams.
Extra awesome
- Experience working in a B2B SaaS product company.
- Experience with orchestration tools such as Airflow or Argo Workflows.
- Familiarity with SQL, Spark, Snowflake, or other data processing technologies.
- Experience building systems that combine structured data, unstructured data, and generative AI.
- Experience with AI security, privacy, responsible AI, prompt injection protection, or data leakage prevention.
- Experience improving the latency and cost of AI systems operating at scale.
- 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.
We 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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Typeform, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Typeform AI Hiring
Typeform has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $210K - $210K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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