Machine Learning Engineer - Autonomy at Wayve

London, England, United Kingdom

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, AutomotiveIndustries

Requirements

Candidates should have 7+ years (Staff) or 10+ years (Principal) of experience in ML engineering with a proven record of shipping deep learning systems to production. Expertise in deep learning, particularly in sequential models, control, planning, or perception, is essential, along with proficiency in Python, C++, CUDA, and ML frameworks like PyTorch. Experience with real-time systems or robotics, including simulation- or vehicle-in-the-loop components, is required. The ability to lead technical initiatives, drive alignment, and mentor engineers is also necessary. Desirable qualifications include prior work in autonomous driving, imitation learning, trajectory prediction, personalization, human behavior modeling, driver intent inference, and experience integrating ML systems into production hardware or multi-agent simulation.

Responsibilities

The Machine Learning Engineer will develop and improve end-to-end driving models for performance, robustness, and generalization, and lead projects on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment. They will build evaluation pipelines and metrics for driving performance and product readiness, curate and mine data for scenario diversity and feature development, and influence architecture choices, training methodologies, and deployment pathways for production-scale learning systems. The role involves collaborating cross-functionally to ensure integration and iteration velocity, and mentoring senior engineers while shaping the long-term technical direction of the Autonomy team.

Skills

Machine Learning

Model Architecture

Data Pipelines

Autonomous Driving

Software Development

Wayve

Develops autonomous vehicle technology using AI

About Wayve

Wayve.ai develops self-driving technology known as AV2.0, which focuses on creating a smarter and safer approach to autonomous vehicles. Their technology uses embodied AI software that allows vehicles to learn from their experiences and adapt to different environments without needing detailed programming. This method is different from traditional self-driving technologies that often rely on expensive hardware and pre-mapped data. Instead, Wayve.ai employs end-to-end deep learning, making their solution more cost-effective for automakers. The company targets automakers and fleet operators, offering them adaptable and affordable solutions for driving automation. Wayve.ai has already partnered with major retailers in the UK to test its technology in delivery fleets, aiming to enhance mobility and sustainability in the automotive industry.

London, United KingdomHeadquarters

2017Year Founded

$1,272.3MTotal Funding

SERIES_CCompany Stage

Automotive & Transportation, AI & Machine LearningIndustries

201-500Employees

Benefits

Hybrid Work Options

Risks

Increased competition in San Francisco could dilute Wayve's market presence.

Regulatory challenges may delay the deployment of Wayve's technology with Uber.

Wayve's reliance on AI systems may face skepticism from traditional automakers.

Differentiation

Wayve uses embodied AI to adapt vehicles to any environment without explicit programming.

Its AV2.0 technology eliminates the need for costly robotic stacks and complex mapping.

Wayve's end-to-end deep learning approach offers a cost-effective solution for automakers.

Upsides

Wayve's partnership with Uber expands its market reach and data collection opportunities.

The expansion into the U.S. market taps into a larger talent pool and partnerships.

Generative AI models like GAIA-1 offer new ways to simulate driving scenarios.

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