Senior Machine Learning Engineer, Vision Models
About the role
🛠️ About our Engineering Teams
About the Measurement Team
🧠 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.
🧩 What you’ll be working on
- 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 should apply if
- 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.
🌱
Not ticking every box? That’s totally okay!
More about Wayve
How we work 💻 — Locations & Flexible Working
🔍 The Interview Process
- 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)
We'll always explain the format and work around your availability.
What’s in it for you (location dependent)
- 💰 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
A quick, honest note before you apply. Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you'll help write it. That suits people who want real ownership more than people who need a settled structure from day one.
If that sounds like the kind of problem you want to spend your time on, we'd really like to hear from you.
At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.
For more information visit Careers at Wayve. To learn more about what drives us, visit Values at Wayve
DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an 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.
Not included in the source posting: qualifications, benefits.
Skills
Who can apply
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