23 hours ago
Base Salary
$311k - $419k/yr
Responsibilities
- Design, build, and maintain scalable machine learning pipelines for data ingestion, model training, evaluation, and research workflows.
- Build software systems that scale ML workloads to larger datasets, models, and experiment volumes.
- Develop reusable interfaces connecting data, models, training, evaluation, and downstream robotics workflows.
- Own codebase architecture, testing, reliability, maintainability, and engineering standards for the MEGA team.
- Identify and resolve performance, reliability, and usability bottlenecks across machine learning workflows.
- Build infrastructure supporting multiple researchers and ML projects.
- Collaborate with researchers to translate new model and experiment requirements into practical software solutions.
- Build and use distributed training and data-processing pipelines for large models and multimodal datasets.
Requirements
- Strong software engineering skills and experience building high-quality, maintainable software.
- Experience building and maintaining machine learning pipelines or infrastructure, including data ingestion, training, evaluation, or experiment workflows.
- Experience designing reliable, reusable, and adaptable software systems and abstractions.
- Hands-on experience with modern machine learning frameworks and ML training and experimentation workflows.
- Strong debugging skills across complex machine learning systems.
- Experience with software testing, code quality, and maintaining a healthy shared codebase.
- Experience with large datasets, large models, or computationally demanding machine learning workloads.
- Ability to collaborate with researchers and engineers and translate research requirements into software systems.
- Preferred experience with distributed training, multi-node systems, or large-scale data processing.
- Preferred experience supporting foundation-model training or large-scale machine learning research.
- Preferred experience with multimodal models, video models, vision-language models, robotics, embodied AI, simulation, or robot-interaction data.
- Preferred experience building infrastructure in fast-moving applied research environments and improving ML system performance, reliability, or developer experience.
Benefits
- Full-time hybrid role based in Sunnyvale, California, combining office and workshop collaboration with work from home.
- Competitive equity package in addition to base salary.
- Core working hours with flexibility to determine a schedule that works for the team.
- Inclusive interview process with accommodations available upon request.
Categories
Data EngineeringML Engineering
About Wayve
Wayve builds end-to-end autonomous driving software—the vehicle-agnostic Wayve AI Driver—that runs on onboard compute and native sensors, licensed to automakers and fleet operators. Its platform spans ADAS and higher autonomy (L2+/L3 to robotaxi) and is designed to generalize across vehicle types and geographies. Founded in 2017 and headquartered in London, it tests its models across Europe, North America, and Japan, with a U.S. base in Sunnyvale, CA.