Overview
As an ML Software Engineer on the MEGA team, you will help build the software foundation for foundation models that enable robots to perceive, reason, and act in the real world. You’ll design scalable ML pipelines and shared tooling to accelerate research across data, models, and robotics workflows. You’ll work closely with researchers to translate evolving needs into robust software systems and maintain a healthy, reusable codebase. This role offers high-impact work at the intersection of video, language, and robot interaction data, with ownership over core parts of the ML stack. You’ll contribute to Wayve’s mission to create universal autonomy through embodied AI and scalable deployment.
Pay / Benefits
- market benchmarking salaries
- equity ownership
- relocation support and visa sponsorship
- hybrid working with core hours
- learning and development budget
- comprehensive benefits (health, dental, wellbeing)
Responsibilities
- Design and maintain scalable data ingestion, model training, evaluation, and experimentation pipelines
- Build reusable interfaces and tooling across data, models, training, evaluation, and robotics workflows
- Collaborate with researchers to translate model and experiment needs into software systems
- Identify and resolve performance, reliability, and usability bottlenecks in large-scale ML workloads
- Improve shared codebase quality through architecture, testing, and documentation
- Support distributed training and data processing for large video, language, and robot-interaction datasets
- Own and improve MEGA codebase health, architecture, testing, and engineering standards
- Develop infrastructure enabling multiple researchers and projects to iterate efficiently
Key requirements
- Strong software engineering skills with focus on maintainable code
- Experience building and maintaining ML pipelines or infrastructure (data ingestion, training, evaluation, or experiment workflows)
- Ability to design reliable, reusable software abstractions that adapt to changing requirements
- Hands-on experience with modern ML frameworks and understanding of ML training workflows
- Strong debugging skills across large ML systems
- Experience with software testing, code quality, and healthy codebase practices
- Experience working with large datasets and computationally demanding ML workloads
- Ability to collaborate with researchers and translate requirements into software solutions
- Collaborative mindset
- Effective communication with researchers and engineers
- Problem-solving and debugging under complex systems
- Modern ML frameworks
- ML pipelines and infrastructure
- Distributed training and data processing
…