Senior Machine Learning Engineer

The Opportunity

As a Senior Machine Learning Engineer at Veo you will get to work on challenging problems and directly contribute to the value created by our AI-driven end-user products.

You will become part of our AI team, which comprises 20+ researchers and engineers responsible for all stages in the machine learning lifecycles across different projects - from scoping and defining data annotation tasks to modeling, validation, and deployment. You will take ownership of the direction and execution of your projects while collaborating closely with the rest of the team, exchanging feedback, and helping others improve and succeed.

We stay current with the latest research and continuously discuss concepts and ideas in recent papers to assess their relevance to our tasks and product-specific challenges.

We are looking for an ambitious, high-performing senior engineer or researcher with a strong track record of delivering real-world machine learning systems. You should bring the technical depth and judgment needed to make sound decisions in complex and sometimes ambiguous environments.

What you will do:

• Train state-of-the-art machine learning models
• Write efficient inference pipelines for cloud and edge
• Define and supervise data annotation tasks
• Improve evaluation schemes to increase understanding of model performance and shortcomings
• Contribute to long-term planning and prioritization of tasks
• Mentor colleagues and raise the technical bar through feedback, knowledge sharing, and collaboration

What you could bring:

• MSc or PhD in a relevant field, or equivalent practical experience
• Extensive hands-on experience delivering real-world, large-scale machine learning projects
• Strong coding and software engineering skills, with experience building maintainable production systems
• Experience taking ownership of technically complex projects from concept to deployment
• Sound technical judgment and the ability to navigate ambiguity and balance research, product, and engineering considerations
• A rigorous and critical approach to examining details, assumptions, and results
• Strong communication skills and the ability to influence technical direction across teams