Our industry is evolving fast, and staying relevant takes constant innovation. At Coveo, that innovation is visible in the way our teams collaborate and in the solutions we build to help customers achieve real, measurable results. In fact, much of this innovation comes to life within our machine learning teams.
Why you should be building AI at Coveo
Coveo helps enterprises transform complex, unstructured data into accurate intelligence that makes people, applications, and AI systems smarter. With nearly two decades of innovation and over a decade of applied AI expertise, our product runs in production for 700+ organizations across technology, healthcare, financial services, retail, and manufacturing.
Ever wondered what it is actually like to be on the team building the AI behind all of this?
A good place to start is by understanding what Coveo’s ML team does
Building AI is exciting. Building AI that reaches production, solves real problems and creates measurable impact for hundreds of organizations is even better.
Our machine learning teams tackle some of the most complex challenges in search, personalization, recommendations and generative AI. Their work shapes the search, commerce and support experiences people interact with every day, influencing how results are ranked, how recommendations are generated and how content is personalized. The systems they build help millions of people find information faster, discover relevant products and get better support experiences through cutting edge agentic technology and the A2UI protocol.
Our machine learning engineers own the entire lifecycle of a model, from digging into a business problem, testing new concepts through A/B testing and validating a proof of concept. And when a model proves its value, the next question becomes: how can we scale it for broader use?
And that’s where things get interesting. Coveo’s models are not built for one use case. Every customer context is different. Our ML team designs AI models that can adapt across industries, use cases, languages, traffic volumes and data structures. One day, the challenge might be personalization in customer support: helping a company anticipate what customers need before they even ask, using signals from past interactions, loyalty data and user behavior. The next, it might be relevance at scale: helping an e-commerce company tune search results across a massive product catalog.
What kind of person thrives here
The people who thrive on Coveo’s ML team tend to have one thing in common; curiosity. They are the kind of people who do not stop at “it works.” They want to understand why it works, why it failed the first time, what signal was missed and what could make the model even better.
Curiosity matters because the problems are rarely straightforward. The people who do well here are creative in how they approach challenges, comfortable with ambiguity and persistent when the answer is not obvious. They take ownership, keep questioning and look for smarter ways to improve what they build.
Of course, strong technical foundations matter too. The team works on large-scale systems where performance, experimentation and scalability are constantly in play. Engineers here need to think through tradeoffs, debug complex systems and keep improving models that are meant to perform in the real world.
They are also genuinely passionate about the field. They follow what is happening in AI, explore new technologies out of curiosity and get excited about what is coming next.
The techstack
To stay ahead in the rapidly evolving field of AI search, our ML team builds on a cloud native architecture designed for large scale indexing, low latency search, secure content access and real time behavioral signals.
Coveo runs its search infrastructure across AWS regions, with automated health checks, AWS Global Accelerator, index fallback mechanisms and resilience testing to reduce downtime risk without requiring customers to change their implementation.
The core ML framework is built around Python and modern deep learning frameworks such as PyTorch.The team relies on a broad technical toolkit designed for textual data. They leverage classical and modern natural language processing techniques (NLP), generative AI through large language models (LLM), information retrieval techniques such as retrieval-augmented generation (RAG) and state-of-the-art vector representations used in semantic search. They are also starting to bring image data into the picture, opening the door to experiences where models can understand both the words users type and the visuals they interact with.
Why you should join
Coveo is a Canadian tech company recognized by Fast Company as one of the best workplaces for innovators and named a Leader by both Gartner and Forrester.
If you ask the people already on the team what drew them to Coveo, the answers tend to cluster around the same things: the technical depth of the work, the scale, and the opportunity to solve hard problems alongside smart colleagues.
If you want to build AI that goes beyond prototypes and actually runs in production at enterprise scale, we’d love to meet you.
Explore our open roles and see where your next challenge could take you at Coveo.