Hi! Before diving into the job details, let's give you some context on who we are and what to expect when joining the team.

Typeform, launched in 2012 in Barcelona, drives more than 500 million digital interactions per year and integrates with hundreds of other business-critical tools. We’re expanding from a small Barcelona-based business to a truly international and remote company. We’re hiring talent across Spain, the United States, the United Kingdom, Germany, Colombia, France, Ireland, the Netherlands and Portugal. Be aware, the location is to be subjected to change, depending on the role.

Our vision is a world of more personal business relationships. Through our no-code Saas platform, we believe we can create that world by living our mission: To bring people closer with better conversations. We turn digital interactions into human connections, by offering people-friendly forms, quizzes, surveys, and asynchronous video solutions.

To do so, we look for people who are curious, ready to own their objectives, and passionate about taking organisations to their next chapter. But we are aware it’s not for everyone, our environment is perfect for those willing to become a change agent and roll up their sleeves to build our rocket ship.

About the Team

The Data Science team at Typeform, located in the EMEA region, consists of highly skilled professionals dedicated to leveraging data and insights to drive impactful decision-making. At the core of this team is our Machine Learning Engineer (MLE), who plays a vital role in solving complex problems using cutting-edge machine learning techniques and algorithms.

As a valued member of the Data Science team in the Data and Insights division at Typeform, you will have the opportunity to collaborate closely with a cohesive and talented group of individuals. Reporting directly to Carmen Herrero, the Machine Learning Engineer (MLE), you will work alongside three skilled data scientists, one additional machine learning engineer, and an experienced manager.

Currently, the team consists of 2 data scientists and a manager, but the ideal composition is a total of five members, including the backfills. By adding an extra data scientist and an additional machine learning engineer, the team will reach its intended structure of 3 data scientists, 1 machine learning engineer, and 1 manager. This expansion ensures a diverse set of expertise and perspectives, enabling the team to effectively leverage the power of data for the organization's benefit.

The team is based in the EMEA region and operates as a dynamic and collaborative unit, working together to drive impactful insights and solutions through advanced data science practices.

With a primary focus on data, the team's scope of influence extends across various areas within Typeform. They actively collaborate with stakeholders from different departments, supporting them in making data-driven decisions that contribute to the organization's success and create tangible value.

Through their collaborative efforts, the team strives to enhance the customer experience, optimize operational efficiency, and drive innovation, all while ensuring the organization's long-term growth and strategic objectives are met. By harnessing the potential of advanced machine learning models and techniques, the team empowers Typeform to stay ahead in a rapidly evolving landscape.

As part of this dynamic team, the MLE and their colleagues combine their expertise, passion for data, and dedication to solving intricate problems, making them an integral force behind our data-driven culture. Together, they work tirelessly to unlock the insights hidden within vast datasets, enabling us to navigate the ever-changing world of business with confidence and intelligence.

About the Role

The machine learning engineer role aims to solve complex problems using machine learning techniques and algorithms. These problems may include developing predictive models, building recommendation systems, automating processes, improving data quality and accuracy, and optimizing algorithms for performance.

The strategic purpose of the MLE is to leverage machine learning and data science techniques to drive innovation, gain competitive advantage, and create value for the organization. By developing and deploying machine learning models, the role contributes to the organization's long-term growth and strategic objectives, such as improving customer experience, optimizing operations, and making data-driven decisions.

Things you will do:

• Solve complex problems using machine learning techniques and algorithms.
• Develop predictive models, build recommendation systems, automate processes, improve data quality and accuracy, and optimize algorithms for performance.
• Leverage machine learning and data science techniques to drive innovation, gain competitive advantage, and create value for the organization.
• Contribute to the organization's long-term growth and strategic objectives by developing and deploying machine learning models.
• Design, implement, and maintain machine learning models and systems.
• Collaborate with data scientists and data engineers to understand business requirements, gather and preprocess data, select appropriate algorithms, train and evaluate models, and deploy them into production environments.
• Monitor model performance, conduct experiments, fine-tune algorithms, and ensure scalability and reliability of the machine learning infrastructure.
• Handle all code-related tasks related to machine learning.

What you already bring to the table:

• Strong understanding of machine learning algorithms and techniques
• Proficiency in programming languages such as Python
• Knowledge of data manipulation and preprocessing techniques
• Experience with machine learning libraries and frameworks like scikit-learn
• Understanding of statistical concepts and methodologies
• Familiarity with database systems and SQL
• Ability to work with big data and distributed computing frameworks like Apache Spark
• Experience in model evaluation, validation, and optimization
• Understanding of software engineering principles and best practices
• Knowledge of cloud computing platforms and services like AWS

Extra awesome:

• Experience with deep learning architectures and frameworks
• Understanding of natural language processing (NLP) and text mining techniques
• Familiarity with reinforcement learning concepts and algorithms
• Experience with deployment and productionization of machine learning models
• Understanding of containerization technologies like Docker
• Knowledge of version control systems like Git
• Familiarity with DevOps practices and continuous integration/continuous deployment (CI/CD) pipelines
• Understanding of distributed computing and parallel processin

*Typeform drives hundreds of millions of interactions per year, powering conversational, human-led experiences all over the world. We are proud to be an equal-opportunity employer. We celebrate diversity and do not tolerate discrimination and harassment of any kind, regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. We believe that by celebrating our differences, we can win together.