Do you want to turn cutting-edge natural language processing into products impacting millions of users? As a Senior Applied Scientist in the Product Engineering group, you will help build the language technologies at the core of our AI products.
You’ll be contributing directly to the design, evaluation, and implementation of advanced AI solutions at the intersection of Retrieval Augmented Generation (RAG), information retrieval, semantic search, question answering, and agentic systems, always with real-world constraints in mind.
As one of our Senior Applied Scientists (NLP), you will:- Design, develop, and optimize modern language-based AI solutions, from data and modeling choices to evaluation strategies and production constraints and guardrails.
- Apply and serve transformer-based language models at scale, delivering reliable, real-time experiences to enterprise users.
- Collaborate closely with machine learning developers and the machine learning platform team to accelerate research and innovation cycles.
- Contribute to research and development processes that foster efficiency, rigor, and a culture of applied science excellence.
- Mentor junior applied scientists, supporting their growth and helping raise the overall level of expertise within the group.
- Promote promising research and best practices in our applied science community.
- Share our innovations externally through technical blog posts, presentations, or other thought leadership content.
- At least 4 years of industry experience applying NLP to production systems.
- Demonstrated skill in evaluating and improving LLM-based systems: building test sets, defining offline and online metrics, using LLM-as-judge methods with awareness of their limits, and catching regressions in non-deterministic systems.
- Strong Python skills and fluency with typical tools (HuggingFace, Pandas/Polars, MLFlow), with the habit of writing tested, maintainable code that runs in production.
- Familiarity with cloud infrastructure environments for Machine Learning such as AWS, Azure or GCP.
- Experience working with AI coding assistants in your own development workflow, and a point of view on where they help and where they don’t.
- Proven ability to collaborate effectively with other functions: You can clearly explain scientific risks and technical tradeoffs to Product Managers, Designers, and Software Developers.
- Experience building agentic systems: tool use, multi-step planning, orchestration, guardrails, and especially how you evaluated them and handled failure modes.
- Familiarity with traditional lexical and linguistics-based approaches, including classical Information Retrieval and Search
- Experience with the safety and trust side of enterprise AI, such as data governance and legal compliance
- International work experience and a demonstrated ability to collaborate across cultures and perspectives.
Do you think you can bring this role to life?
Join the Coveolife!
We encourage all qualified candidates to apply regardless of, for example, age, gender, disability, gaps in CV, national or ethnic background.
This job description was written by humans, assisted by AI. We may leverage technology in our hiring process to help us see the person behind the resume.
Coveo is committed to providing accessible employment practices. If you require accommodation due to a disability at any point during the recruitment process, please contact [email protected] to discuss your needs.
Skills Required
- At least 4 years of industry experience applying NLP to production systems
- Experience evaluating and improving LLM-based systems, including test sets, offline and online metrics, LLM-as-judge methods, and regression detection
- Strong Python skills
- Fluency with Hugging Face, Pandas or Polars, and MLflow
- Experience writing tested, maintainable production code
- Familiarity with AWS, Azure, or GCP machine learning infrastructure
- Experience using AI coding assistants in a development workflow
- Ability to collaborate with Product Managers, Designers, and Software Developers and explain scientific risks and technical tradeoffs
- Experience building agentic systems involving tool use, multi-step planning, orchestration, guardrails, evaluation, and failure-mode handling
- Familiarity with classical information retrieval, search, lexical, and linguistics-based approaches
- Experience with enterprise AI safety, data governance, and legal compliance
- International work experience and cross-cultural collaboration experience
Coveo Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Coveo and has not been reviewed or approved by Coveo.
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Healthcare Strength — Healthcare is considered comprehensive and good value, spanning medical, dental, vision, disability, mental‑health support, telemedicine, and an EAP. This breadth and perceived affordability stand out across locations.
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Leave & Time Off Breadth — Paid holidays, PTO/sick leave, volunteer time off, and work‑from‑home flexibility are commonly available. Flexible working hours and solid vacation policies in Canada further support work‑life balance.
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Parental & Family Support — Parental‑leave top‑ups, options to extend leave, and on‑site daycare in Quebec City indicate robust family support. These provisions complement caregiver‑friendly flexibility in day‑to‑day scheduling.
Coveo Insights
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.
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