About the job
At Intercom, we are revolutionizing AI Customer Service with the mission to empower businesses to deliver exceptional customer experiences.
Our advanced AI agent, Fin, stands at the forefront of customer service technology, enabling organizations to provide consistent and outstanding support. Fin seamlessly integrates with our Helpdesk, forming the Intercom Customer Service Suite, a comprehensive solution designed to tackle complex inquiries that require a human touch.
Since our inception in 2011, we have gained the trust of nearly 30,000 businesses worldwide, setting new benchmarks for customer service. Our commitment to our core values drives us to innovate rapidly and deliver remarkable value to our clients.
What’s the Opportunity?
The Machine Learning team at Intercom is pivotal in defining innovative ML features, exploring optimal algorithms, and swiftly deploying prototypes to enhance customer interactions.
We prioritize product focus and collaborate closely with Product and Design teams. Our specialized ML product engineers facilitate rapid production deployment, often launching beta versions just weeks after successful offline testing.
Our enthusiasm for machine learning is evident in our diverse applications—from traditional supervised models to cutting-edge unsupervised clustering techniques and novel transformer neural networks. We rigorously assess the customer impact of every model we implement.
What Will I Be Doing?
Actively participate in the recruitment, mentorship, and career development of fellow engineers.
Elevate technical standards, performance, reliability, and operational excellence.
Identify opportunities where machine learning can add value for our customers.
Collaborate with teammates and stakeholders in Product and Design to frame product challenges appropriately.
Conduct thorough exploratory data analysis to gain insights into problem areas.
Research and select the most effective algorithms and tools while balancing pragmatism and innovation.
Perform offline evaluations to validate algorithm effectiveness.
Collaborate with engineers to transition prototypes into production.
Plan, measure, and communicate findings to inform iterative improvements.

