Senior Machine Learning Developer

Key Skills

Machine learning techniques, Python Programming, PyTorch, Snowflake, TensorFlow

Job Description

Streamline the deployment of machine learning at scale

How do you turn cutting-edge machine learning research into production-ready systems that perform reliably at scale? As a Senior Machine Learning Developer, you will support applied scientists by building robust, scalable machine learning systems that accelerate experimentation and power real-world products.

You will use your technical leadership to help shape Coveo’s ambitious vision, bringing the latest advances in machine learning to enterprise commerce, self-service, and other business-critical use cases.

As one of our Senior Machine Learning Developers, you will:

• Streamline the end-to-end lifecycle of machine learning models, from conception and implementation to automated testing, deployment, and monitoring.
• Analyze and improve the performance of machine learning models and systems, meeting strict service-level objectives for large-scale training and low-latency inference.
• Maintain and evolve critical, client-facing product features powered by machine learning.
• Enable teams to adopt machine learning platform, observability tools, and best practices that improve operational efficiency and service reliability.
• Collaborate with peers to challenge existing approaches, improve ways of working, and influence architectural decisions across the organization.
• Contribute to the evolution of a modern technology stack including Python, Amazon Web Services, Kubernetes, PyTorch, Terraform, Snowflake, Honeycomb, and more.

Here is what will qualify you for the role:

• 5 or more years of industry experience working with machine learning systems.
• Hands-on experience deploying, monitoring, and supporting artificial intelligence models in production at meaningful scale.
• Strong foundations in data and software engineering, with a focus on building reliable, maintainable production systems.
• Experience collaborating closely with scientists and cross-functional teams to improve tooling, workflows, and overall delivery efficiency.

What will make you stand out:

• Deep expertise in machine learning operations, machine learning engineering, and large-scale model deployment.
• Experience structuring data lifecycles and data logic within machine learning projects.
• A track record of maintaining, improving, and promoting shared internal tools or libraries.
• Knowledge of domains such as natural language processing, information retrieval, or recommendation systems, and the implications of building pipelines for these use cases.

Do you think you can bring this role to life? Or add your own color?

You don’t need to check every single box; passion goes a long way and we appreciate that skillsets are transferable.

Send us your application, we want to hear from you!