What if your product decisions could shape how every machine learning model at Coveo is built, deployed and operated? As a Technical Product Manager in our ML Platform unit, you'll help shape the platform our applied scientists and ML engineers rely on to train, evaluate, deploy, serve and monitor the models and AI capabilities behind Coveo's products.
Working alongside the unit's director, dev managers, technical leads, applied scientists and product managers, you'll turn complex needs into clear priorities and actionable plans, balancing what our internal users need today with reliability, cost, scalability and the long-term health of the platform.
As one of our Technical Product Managers, you will:
- Help define and evolve the ML Platform product roadmap, balancing the needs of our ML and AI teams with reliability, cost efficiency, scalability and technical debt.
- Partner closely with technical leads across model building, model serving, experimentation and the LLM platform, validating direction with them before committing teams to it.
- Understand how applied scientists and ML engineers work, and reconcile their needs into feature briefs, written proposals and clear priorities.
- Stay current on MLOps, LLMOps and AI infrastructure trends, and bring build versus buy options to the table with evidence.
- Lead product discovery for new capabilities, helping teams validate technical approaches and user value before work starts.
- Coordinate across teams and partners such as Security, Legal and Data Platform to manage dependencies, track progress and ensure successful delivery.
Here is what will qualify you for the role:
- 5+ years of experience in technical product management or a closely related role, including owning a roadmap and setting priorities, not only managing delivery.
- Hands-on understanding of machine learning systems in production (training pipelines, deployment and serving, monitoring), gained through a software engineering, ML engineering or data science background.
- Strong communication and stakeholder management skills, with the ability to write clear proposals and build alignment across technical and non-technical audiences.
- A proactive problem-solving mindset, with the habit of validating ideas early with technical experts and users before committing to them.
What will make you stand out:
- Experience as a product manager of an internal ML, data or developer platform whose customers are engineers or data scientists.
- Familiarity with MLOps and LLMOps tooling such as MLflow, SageMaker, Kubernetes-based serving, model monitoring, LLM gateways and evaluation.
- Experience driving platform adoption, including deprecating legacy tools and migrating teams to new ones.
- A track record of improving product practices in teams that worked without a product manager, and of building trust with experienced engineers.
Do you think you can bring this role to life? Send us your application, we want to hear from 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.
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
- 5+ years of experience in technical product management or a closely related role
- Experience owning a product roadmap and setting priorities
- Hands-on understanding of production machine learning systems, including training pipelines, deployment, serving, and monitoring
- Strong communication and stakeholder management skills
- Ability to write clear proposals and build alignment across technical and non-technical audiences
- Proactive problem-solving mindset and experience validating ideas with technical experts and users
- Experience managing an internal ML, data, or developer platform used by engineers or data scientists
- Familiarity with MLOps and LLMOps tools such as MLflow, SageMaker, Kubernetes-based serving, model monitoring, LLM gateways, and evaluation
- Experience driving platform adoption, deprecating legacy tools, and migrating teams to new tools
- Experience improving product practices in teams without a product manager
- Experience building trust with experienced engineers
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.
