About the job
Intercom is an innovative AI Customer Service company dedicated to empowering businesses to deliver exceptional customer experiences.
Our state-of-the-art AI agent, Fin, is recognized as the leading customer service AI solution available. It enables businesses to provide consistent, high-quality customer service, thereby enhancing their overall customer experience. Fin can seamlessly integrate with our Helpdesk to form the comprehensive Intercom Customer Service Suite, which offers AI-enhanced support for more complex queries that necessitate human intervention.
Founded in 2011 and trusted by nearly 30,000 businesses worldwide, Intercom is redefining the customer service landscape. Guided by our core values, we continuously challenge the status quo, act with urgency, and strive to deliver remarkable value to our clients.
What is the Opportunity?
Our Machine Learning team at Intercom is pivotal in identifying and developing new ML features, exploring suitable algorithms and technologies, and swiftly delivering initial prototypes to our customers.
We are a highly product-focused team, collaborating closely with our Product and Design partners. The dedicated ML product engineers on our team enable us to expedite our transition to production, often launching beta versions just weeks after successful offline testing.
We are deeply passionate about harnessing machine learning technology, having productized a range from traditional supervised models to state-of-the-art unsupervised clustering algorithms and innovative applications of transformer neural networks. We rigorously test and evaluate the tangible customer impact of each model we deploy.
What Will I Be Doing?
Actively participate in recruitment, mentoring, and career development initiatives for fellow engineers.
Elevate technical standards, performance metrics, reliability, and operational excellence.
Pinpoint opportunities where ML can deliver significant value to our customers.
Collaborate with team members and Product and Design stakeholders to define the appropriate ML framework for product challenges.
Conduct exploratory data analysis and research to gain a deep understanding of the problem domain.
Investigate and select suitable algorithms and tools, balancing practicality with cutting-edge innovation where necessary.
Execute offline evaluations to validate algorithm efficacy.
Collaborate with engineers to transition prototypes to production.
Plan, measure, and disseminate insights to guide iterative improvements.

