As a Senior Machine Learning Developer in R&D, you will play a key role in supporting the teams of applied scientists by building ML systems that are reliable, scalable and easy to maintain, as well as helping them iterate on experiments faster.
Your mission? Use your technical leadership influence to guide our ambitious AI technological vision of bringing the latest advances in the domain to a wide range of enterprise commerce clients.
Here is what makes this opportunity exciting:The ML unit at Coveo focuses on finding ways to apply the latest advances in Recommender Systems, Ranking Optimization, LLMs and NLP to build innovative solutions in e-commerce, self-service and other business verticals.
We solve real problems with real data, for hundreds of large enterprise clients all around the world, on a modern platform that serves over 100M requests and automatically trains thousands of ML models on a daily basis.
Here is a glimpse at your responsibilities:- Help streamline the development life-cycle of ML models: conception, implementation, automated testing, deployment, monitoring, etc.
- Analyze and improve the performance of our models and systems – including meeting critical SLOs for training models at scale and low-latency inference.
- Maintain and improve several of our most important client-facing product features.
- Facilitate the adoption and utilization of ML platform and observability resources and guidelines to improve operational efficiency and service reliability.
- Engage with your community of peers to challenge the status quo, improve our shared ways of working, and influence overall architecture decisions.
- Evolve our modern tech stack which includes Python, AWS, Kubernetes, Pytorch, Terraform, Snowflake, Honeycomb and others
- You have 5+ years of Machine Learning industry experience.
- You have operationalized, instrumented and supported AI models in production at a non-trivial scale before
- You are fluent in good data and software engineering practices, and you are able to develop the tools and culture which enable your team to deliver reliable production code in an efficient manner.
- You enjoy collaborating with scientists on a daily basis to understand their pain points and figure out how to improve their tools and increase their efficiency. You also have experience working in cross-functional teams.
- You master best practices in ML-Ops, ML engineering, and large-scale deployment of ML models.
- You are familiar with Data Lifecycle in ML and helped structure the data logic in ML projects before.
- You have experience maintaining and evangelizing internal resources and libraries.
- You are savvy in specific ML domains such as Natural Language Processing, Information Retrieval, Recommendation Systems, and the technical implications of building pipelines for such use cases.
- You are recognized for your communication skills and presenting complex technical subjects to audiences with different levels of technical proficiency.
You don’t need to check every single box; passion goes a long way and we appreciate that skillsets are transferable.
Send us your CV, we want to get to know you! Join the Coveolife!
We encourage all qualified candidates to apply regardless of, for example, age, gender, disability, gaps in CV, national or ethnic background. We know that applying for a new role is a lot of work and we really appreciate your time.
Top Skills
What We Do
Coveo powers the digital experiences of the world’s most innovative brands serving millions of people and billions of interactions across every digital experience. After a decade of enriching our market-leading platform with forward-thinking global enterprises, we know what it takes to gain a trusted AI-experience advantage.
We strongly believe that the future is business-to-person, that experience is today’s competitive front line, a make or break for every business.
For enterprises to achieve this AI-experience advantage at scale, it is imperative to have an Enterprise Spinal and composable ability to deliver AI semantic search and generative experiences at each customer and employee interaction.
