Machine Learning Software Engineer
$312k - $420k • Sunnyvale, California USA • Full-time
Posted 28d ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Wayve is seeking a founding Machine Learning Software Engineer for its Multi-Embodiment Generalist Agent (MEGA) team. This role focuses on building foundation models for general-purpose robots, enabling them to learn from diverse data and generalize across tasks and embodiments. You will be instrumental in designing and constructing the software systems that support MEGA's machine learning research, including scalable ML pipelines, data and training infrastructure, and shared tools for rapid experimentation. This position offers a unique opportunity to work closely with large-scale video and robotics datasets, modern ML frameworks, and advanced models, ensuring the reliability and maintainability of research systems as the program expands. You will own critical components of the software and ML infrastructure, influencing the team's ML stack development and operations.
Responsibilities
- Design and maintain scalable pipelines for data ingestion, model training, evaluation, and experimentation.
- Build reusable interfaces and shared tooling across data, models, training, evaluation, and downstream robotics workflows.
- Partner with researchers to translate evolving model and experiment needs into practical software systems.
- Diagnose and resolve performance, reliability, and usability bottlenecks across large-scale ML workloads.
- Raise the quality of the shared codebase through strong architecture, testing, documentation, and engineering standards.
- Support distributed training and data processing across large video, language, and robot-interaction datasets.
- Build software systems that allow ML workloads to scale to larger datasets, larger models, and more experiments.
- Own and improve the health of the MEGA codebase, including software architecture, testing, reliability, maintainability, and engineering standards.
- Identify and resolve performance, reliability, and usability bottlenecks across ML workflows.
- Build and maintain infrastructure that supports multiple researchers and ML projects without unnecessarily slowing down iteration.
- Work closely with researchers to understand the requirements of new models and experiments, and translate those requirements into practical software solutions.
- Build and use distributed training and data-processing pipelines for large models and large multimodal datasets.
Requirements
- Strong software engineering skills and experience building high-quality, maintainable software.
- Experience building and maintaining machine learning pipelines or infrastructure, such as data ingestion, training, evaluation, or experiment workflows.
- Experience designing software systems and abstractions that are reliable, reusable, and able to evolve as requirements change.
- Hands-on experience with modern machine learning frameworks and a good understanding of how ML training and experimentation workflows operate.
- Strong debugging skills and the ability to investigate problems across complex ML systems.
- Experience with software testing, code quality, and engineering practices for maintaining a healthy shared codebase.
- Experience working with large datasets, large models, or other computationally demanding ML workloads.
- Ability to collaborate closely with researchers and engineers and translate research requirements into practical software systems.