Wayve
Staff Machine Learning Engineer, Vision Models
Sunnyvale, California USA · Staff+ · Full-time
Sponsorship not specified$370k-$407kDetected 21 days ago
PythonMachine LearningDeep LearningPyTorchNLPComputer VisionRoboticsSensorsResearchLeadership
About the role
- You will define ground truth and correctness criteria across a complex driving taxonomy, and turn them into automated benchmarks and evidence that our validation pipelines and safety cases can stand on.
- Offline is where the interesting headroom is: more compute per frame, larger foundation models, and access to both past and future temporal context that the vehicle never has.
- The outputs are mission-critical, directly informing model development decisions and customer deliverables.
Responsibilities
- Develop the models - build, train, and fine-tune the scene understanding models at the centre of Wayve's offline measurement, adapting on-vehicle architectures and Wayve Foundation Models for offline use.
- Drive accuracy and generalisation - improve model performance across vehicle platforms, geographies, and driving conditions
- Measure what you build - benchmark your models, set quality bars, and use metrics and error analysis to steer the next iteration
- treat measurement as the feedback that drives the modelling.
- Drive accuracy and generalisation - improve model performance across vehicle platforms, geographies, and driving conditions; diagnose failure modes and close the loop on blind spots.
Requirements
- Experience with offboard or offline modelling: auto-labelling, model distillation, temporal or world models, or other ways of exploiting compute that on-vehicle systems cannot.
- Experience with fleet-scale data and large-scale distributed training infrastructure.
Compensation
- The reasonably estimated salary for this role ranges from $370,040 to $407,330, plus a competitive equity package.
Benefits
- 5+ years in ML engineering, including training and shipping deep learning models in production, with pathfinding in ambiguous modelling problems from scoping through to a direction others build on.
- Exploit the offline environment - use the advantages the vehicle does not have: higher compute budgets, larger model capacity, bidirectional temporal context, and multi-task or joint representation learning.
- In order to set you up for success as a Staff Machine Learning Engineer at Wayve, we're looking for the following skills and experience.
This listing is sourced directly from Wayve's careers page and normalized into a canonical job model.