At Wayve, the business problem is to create autonomy that propels the world forward by developing Embodied AI technology for automated driving systems.
Requirements
- Strong software engineering skills with experience building and maintaining distributed systems, data pipelines, or backend platforms at scale
- Experience developing infrastructure that supports machine learning workflows—such as training orchestration, evaluation tooling, or inference systems
- Familiarity with technologies like Flyte, Ray, Spark, Airflow, or Kubernetes, and an understanding of how to use them to scale data and compute
- Experience working with large-scale multi-modal datasets (e.g. video, LiDAR, radar, language) and designing systems for ingestion and filtering
- Prior experience in a foundation model or autonomy-focused team, especially in an infrastructure or ML platform role
- Contributions to open-source ML or infra projects (e.g. Flyte, Ray, Dask, MLFlow) or experience with evaluation tooling at scale
Responsibilities
- Design and scale infrastructure for data ingestion, filtering, and curation of multi-modal embodied data
- Build robust, efficient training, evaluation, and inference pipelines to support foundation model development
- Partner closely with scientists and MLEs to accelerate experimentation and unblock research
- Improve ML systems performance, scalability, and automation across the stack
- Act as a cross-functional force multiplier—connecting Science, Software, and Data teams through well-designed tooling and systems
Other
- Full-time role based in our office in Vancouver
- Hybrid working policy that combines time together in our offices and workshops and time spent working from home
- Optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory
- Diversity, equity, and inclusion are valued at Wayve
- Bachelor's, Master's, or Ph.D. degree is not explicitly mentioned but may be required
