Intercom was founded in 2011 to change the standard of customer service online. Our AI-first customer service platform is a totally new way to deliver customer service and is designed to transform the way businesses interact with their customers through AI. We all know that customer service on the internet sucks. It’s slow and impersonal. We help businesses provide instant and exceptional service to their customers and maximize their support agents’ productivity, efficiency, and performance—all through our single AI system. More than 25,000 businesses use Intercom to send millions of messages to millions of customers each month.Intercom has been a long-standing product leader and cultural icon in the technology and startup worlds for more than a decade. We set the pace for our industry and live by our values that allow us to push boundaries, build with speed and intensity, and deliver incredible value to our customers.Join us on our mission to redefine customer service and make internet business personal.
Intercom’s Machine Learning team is responsible for defining new ML features, researching appropriate algorithms and technologies, and rapidly getting first prototypes in our customers’ hands.
We are an extremely product focussed team. We work in partnership with Product and Design functions of teams we support. Our team's dedicated ML product engineers enable us to move to production fast, often shipping to beta in weeks after a successful offline test.
We are very passionate about applying machine learning technology and have productized everything from classic supervised models, to cutting-edge unsupervised clustering algorithms, to novel applications of transformer neural networks. We test and measure the real customer impact of each model we deploy.
Identify areas where ML can create value for our customers
Contribute to finding the right ML framing of a product problem
Working with teammates and Product and Design stakeholders
Taking algorithms which work offline, and putting them in a production setting
Deeply understand and modify as needed
Solve hard scalability and optimization problems
Run production ML infrastructure, evolve it over time
Build new data infrastructure to enable exploration
Establish processes for large scale data analyses, model development, validation, and implementation
Work with teammates to measure and iterate on algorithm performance
Partner deeply with the rest of team, and others, to build excellent ML products
These are meant to be indicative, not hard requirements.
Excellent pragmatic engineering skills
Familiar with tools used to write, test, deploy, debug and monitor software
Comfort owning features from inception to outcome.
5+ years experience in a production environment, with contributions to the design and architecture of distributed systems.
We’re looking for engineers who can confidently put ML-powered features in production.
Strong communication skills, both within engineering teams and across disciplines.
Excellent programming skills
Comfort with ambiguity
BSc in Computer Science, or similar knowledge
Deep knowledge of AWS services
ML Ops experience
Large scale computation experience
Track record shipping ML products
Experience in a research environment
Algorithmic optimisation experience
Advanced education in CS, ML, Math, Stats, or similar
Practical stats knowledge (experiment design, dealing with confounding, etc)
Experience in an applicable ML area. E.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering
Visualization, data skills, SQL, matplotlib, etc.
We are a well treated bunch, with awesome benefits! If there’s something important to you that’s not on this list, talk to us! :)
Competitive salary and equity in a fast-growing start-up
We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen
Regular compensation reviews - we reward great work!
Paid maternity leave, as well as 6 weeks paternity leave for fathers, to let you spend valuable time with your loved ones
If you’re cycling, we’ve got you covered on the Cycle-to-Work Scheme. With secure bike storage too
MacBooks are our standard, but we’re happy to get you whatever equipment helps you get your job done
Policies
Intercom has a hybrid working policy. We believe that working in person helps us stay connected, collaborate easier and create a great culture while still providing flexibility to work from home. We expect employees to be in the office at least two days per week.
We have a radically open and accepting culture at Intercom. We avoid spending time on divisive subjects to foster a safe and cohesive work environment for everyone. As an organization, our policy is to not advocate on behalf of the company or our employees on any social or political topics out of our internal or external communications. We respect personal opinion and expression on these topics on personal social platforms on personal time, and do not challenge or confront anyone for their views on non-work related topics. Our goal is to focus on doing incredible work to achieve our goals and unite the company through our core values.
Intercom values diversity and is committed to a policy of Equal Employment Opportunity. Intercom will not discriminate against an applicant or employee on the basis of race, color, religion, creed, national origin, ancestry, sex, gender, age, physical or mental disability, veteran or military status, genetic information, sexual orientation, gender identity, gender expression, marital status, or any other legally recognized protected basis under federal, state, or local law.
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