Senior Machine Learning Engineer, Vision Models
Wayve
We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that.
Here’s what this particular role covers. ️ About our Engineering Teams Wayve’s engineering teams are building the AI, robotics, simulation, data, and systems foundations needed to deploy a generalisable AI Driver safely and at scale. About The Measurement Team The Measurement team within AI Evaluation builds and qualifies the offline scene-understanding models Wayve uses to measure driving performance after on-road runs and in simulation. Offline modelling provides greater compute, larger models, and access to past and future temporal context, creating unique opportunities to build credible evidence at fleet scale. Your day-to-day- Build, train, and fine-tune scene-understanding models for offline measurement.
- Adapt on-vehicle architectures and Wayve foundation models for offline use.
- Improve accuracy and generalisation across vehicle platforms, geographies, and driving conditions.
- Benchmark your models, diagnose failure modes, and use error analysis to guide iteration.
- Define quality bars and statistically credible evidence for validation pipelines and safety cases.
- Collaborate with on-vehicle modelling, evaluation, data curation, and simulation teams.
- Mentor others and contribute to strong machine-learning and engineering practices.
- Transformer-based, multimodal, and vision-language models for detection, segmentation, classification, and scene understanding.
- Offline models that use higher compute budgets, larger capacity, and bidirectional temporal context.
- Automated benchmarks that assess coverage, mine rare events, and measure driving behaviour.
- Fleet-scale camera, lidar, and simulation datasets.
- Evidence used in model-development decisions, validation pipelines, customer deliverables, and safety cases.
- You have 4+ years in ML engineering and have trained and shipped deep-learning models in production.
- You have hands-on experience with modern computer-vision, transformer, multimodal, or VLM architectures.
- You have adapted or fine-tuned large pretrained or foundation models and understand data, loss, and representation trade-offs.
- You are proficient in Python and PyTorch or a similar framework and comfortable with large-scale training.
- You can take ambiguous modelling problems from scoping to a working, measurable solution.
- Experience in 3D scene understanding, offline modelling, auto-labelling, temporal or world models, autonomous vehicles, robotics, or distributed training is valuable.
- Initial call / recruiter screen (25 min)
- Competency interview (HM Screen; 60 min)
- Deep-dive technical interviews (Applied ML, System Design, Programming; 3 hours)
- Final interview focused on mission and values alignment (60 min)
- Salaries benchmarked against the market annually
- Meaningful equity
- ✈️ Relocation support and visa sponsorship where applicable
- ✅ Hybrid working and access to vehicle workshops and labs
- Learning and development support
- Comprehensive location-dependent health, family, retirement, and wellbeing benefits
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