Machine Learning Software Engineer

As a founding ML Software Engineer on Wayve’s MEGA team, you design and maintain scalable ML infrastructure that underpins foundation models for general-purpose robots. You’ll build data, training, and evaluation pipelines and shared tooling to accelerate research across large-scale video, language, and robot-interaction data. You’ll work closely with researchers to translate requirements into robust software and help scale our ML stack as the program grows. This role offers hands-on influence over the research-to-deployment cycle and the chance to shape the MEGA codebase and tooling.

• Equity tied to company success
• Relocation support and visa sponsorship
• Hybrid working model with core hours
• Learning and development budgets
• Comprehensive health and dental benefits
• Wellbeing and family leave provisions

• Design and maintain scalable data ingestion, training, evaluation, and experimentation pipelines
• Build reusable interfaces and tooling across data, models, training, evaluation, and robotics workflows
• Collaborate with researchers to translate experimental needs into software
• Diagnose and resolve performance, reliability, and usability bottlenecks in large-scale ML workloads
• Improve codebase quality via architecture, testing, and documentation
• Support distributed training and data processing for large, multimodal datasets
• Ensure infrastructure supports multiple researchers and projects without hindering iteration
• Develop pipelines for large models and datasets across video, language, and robot-interaction domains

• Strong software engineering skills and experience delivering maintainable software
• Experience building and maintaining ML pipelines or infrastructure (data ingestion, training, evaluation, experiments)
• Ability to design reliable, reusable software abstractions
• Hands-on experience with modern ML frameworks and ML training/execution workflows
• Strong debugging skills across complex ML systems
• Experience with software testing, code quality, and engineering practices
• Experience working with large datasets or computationally demanding ML workloads
• Ability to collaborate with researchers and translate requirements into software

• Collaboration with researchers and engineers
• Analytical problem-solving
• Clear communication
• Modern ML frameworks
• ML pipelines and infrastructure
• Distributed training and data processing