About the job
Intercom is a pioneering AI Customer Service platform dedicated to enhancing customer experiences for businesses worldwide.
Our advanced AI agent, Fin, is the leading customer service AI on the market, enabling businesses to provide uninterrupted, exceptional customer service, which fundamentally transforms customer interactions. Fin seamlessly integrates with our Helpdesk to form the comprehensive Intercom Customer Service Suite, designed to offer AI-supported assistance for complex queries that necessitate human intervention.
Established in 2011 and trusted by nearly 30,000 global businesses, Intercom is redefining the standard in customer service. Guided by our core values, we challenge norms, innovate with urgency, and consistently provide outstanding value to our clients.
What’s the opportunity?
The Machine Learning team at Intercom is at the forefront of developing new ML features, exploring suitable algorithms and technologies, and swiftly delivering the first prototypes to our customers.
We pride ourselves on being a highly product-focused team. Collaborating closely with Product and Design teams, our dedicated ML product engineers enable rapid production deployment, often launching beta versions within weeks following successful offline testing.
We are deeply passionate about leveraging machine learning technology, with applications ranging from traditional supervised models to innovative unsupervised clustering algorithms, and exploring novel transformer neural network applications. We rigorously evaluate the real-world impact of each model we implement.
What will I be doing?
Identifying opportunities where machine learning can add significant value for our clients.
Determining the appropriate ML framing for product challenges.
Collaborating with team members and stakeholders in Product and Design.
Conducting exploratory data analysis and research.
Gaining an in-depth understanding of the problem domain.
Researching and selecting the most suitable algorithms and tools.
Balancing pragmatism with a commitment to innovation, pushing boundaries when necessary.
Conducting offline evaluations to validate algorithm effectiveness.
Collaborating with engineers to transition prototypes into production.
Planning, measuring, and disseminating findings to guide iterative improvements.
Working closely with the entire team and external partners to create exceptional ML products.
