Why This Job is Featured on The SaaS Jobs
This backend role sits at the intersection of SaaS reliability and applied AI, focused on the kind of internal platform work that enables product teams to ship machine learning features safely. Supporting large-scale model training and high-volume inference aligns with a growing pattern in SaaS: treating ML capabilities as a shared service, with clear expectations around uptime, observability, and repeatable deployment.
For a SaaS career, the durable value here is learning how to operationalize complex workloads in cloud environments and distributed systems. Work that spans performance analysis, deployment automation, and service maintenance builds fluency in the operational disciplines that underpin subscription businesses, where latency and failures translate directly into customer impact. Collaboration with applied scientists and data engineers also provides practical exposure to cross-functional interfaces that are increasingly common as SaaS companies productionize LLMs and other models.
This position is best suited to an engineer who prefers platform-oriented ownership over narrow feature work and is motivated by making systems dependable at scale. It fits someone early to mid-career who can debug with guidance, iterate on process improvements, and stay comfortable moving between Java or Python services, cloud primitives, and infrastructure tooling as the platform evolves.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Build the backbone of AI at scale
Want to see your work power millions of machine learning predictions every day? Join our Machine Learning Platform team and help bring models, including Large Language Models (LLMs), into production in a reliable and scalable way.
You’ll contribute to a platform that trains thousands of models and serves hundreds of millions of queries daily, while growing your expertise in distributed systems and machine learning infrastructure.
As a Backend Developer, you will:
Contribute to the development lifecycle, including coding, testing, and deployment
Support the implementation of scalable solutions to streamline model deployment
Analyze and help improve the performance and reliability of our platform
Participate in maintaining services handling millions of requests per day
Collaborate with applied scientists, data engineers, and developers to integrate models into production
Suggest improvements to existing processes and use AI coding tools to support your development workflow
Here is what will qualify you for the role:
2+ years of experience in backend development in a cloud environment (Java/Spring or Python preferred, AWS an asset)
Good understanding of distributed systems and scalable application design
Ability to solve problems and debug systems with guidance when needed
Comfortable learning new tools, technologies, and best practices
What will make you stand out:
Familiarity with Kubernetes or Terraform
Exposure to machine learning workflows or model deployment
Interest in scalable backend services, large-scale realtime systems, model orchestration or realtime inference at scale
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!
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 HR@Coveo.com to discuss your needs.#li-hybrid
