Senior Machine Learning Developer - MLOps

Summary

Join Coveo's ML Platform team as a Senior Machine Learning Developer and play a key role in shaping MLOps best practices. You will build and maintain tools to support the deployment and productionalization of ML models at scale. This involves creating tooling, libraries, and workflows for efficient model development and collaborating with applied scientists. The role focuses on applying advancements in Recommender Systems, Ranking Optimization, LLMs, and NLP to solve real-world problems for enterprise clients. You'll work with a modern tech stack including Python, AWS, Kubernetes, and more. The position offers the opportunity to influence architecture decisions and improve operational efficiency.

Requirements

• You have 5+ years of Machine Learning industry experience
• You have operationalized, instrumented and supported ML 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 ML teams to deliver reliable production code in an efficient manner
• You enjoy collaborating with scientists 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

Responsibilities

• Provide end-to-end ML tooling from data exploration to production deployment tooling
• Facilitate development, deployment, automated testing, monitoring and debugging of ML models
• Analyze and improve the performance of our models and ML Platform to help meet critical SLOs for training models at scale and low-latency inference
• Facilitate the adoption and usage of ML platform and observability resources and provide 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

Preferred Qualifications

• You master best practices in MLOps, ML engineering, and large-scale deployment of ML models
• You have experience maintaining and evangelizing internal resources and libraries
• You have acquired considerable MLOps experience hosting models at scale, by previously building tooling to facilitate data exploration and experimentation as well as automating and orchestrating complex and efficient training pipelines
• You are recognized for your communication skills and presenting complex technical subjects to audiences with different levels of technical proficiency