About the job
At Intercom, we are revolutionizing customer service through artificial intelligence, empowering businesses to deliver outstanding customer experiences.
Our AI agent, Fin, stands as the most sophisticated customer service AI agent available, allowing companies to provide continuous, flawless customer support, ultimately enhancing customer interactions. Fin seamlessly integrates with our Helpdesk to form the Intercom Customer Service Suite—a comprehensive solution that offers AI-augmented assistance for more complex or high-touch inquiries that necessitate human intervention.
Since our inception in 2011, Intercom has earned the trust of nearly 30,000 businesses worldwide and is setting a new benchmark in customer service. Guided by our core values, we consistently push boundaries, work with speed and intensity, and deliver exceptional value to our clients.
What’s the Opportunity?
The Machine Learning team at Intercom is tasked with defining innovative ML features, researching suitable algorithms and technologies, and quickly delivering initial prototypes to our customers.
Our team is deeply product-focused, collaborating closely with Product and Design teams. Our dedicated ML product engineers enable rapid transitions to production, often launching beta versions just weeks after successful offline testing.
We are passionate about harnessing machine learning technology, having productized everything from traditional supervised models to cutting-edge unsupervised clustering algorithms and novel transformer neural network applications. We rigorously evaluate the real customer impact of each model we deploy.
What Will I Be Doing?
Identifying opportunities where machine learning can add value for our customers.
Defining the appropriate ML framing for product challenges.
Collaborating with teammates and stakeholders from Product and Design.
Conducting exploratory data analysis and research.
Gaining a deep understanding of the problem areas.
Researching and selecting the most suitable algorithms and tools.
Being pragmatic while pushing the boundaries of innovation when necessary.
Performing offline evaluations to validate algorithm effectiveness.
Working alongside engineers to transition prototypes into production.
Planning, measuring, and sharing insights to guide iterative development.
Collaborating closely with the team and others to create exceptional ML products.

