Machine Learning Engineer - Autonomy at Wayve

Sunnyvale, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, AutomotiveIndustries

Requirements

Candidates should have 7-10+ years of ML engineering experience with a proven track record of deploying 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 also required, as is the ability to lead technical initiatives and mentor engineers. Prior experience in autonomous driving, imitation learning, trajectory prediction, personalization, human behavior modeling, or driver intent inference is desirable.

Responsibilities

The Machine Learning Engineer will develop and enhance end-to-end driving models for state-of-the-art performance, robustness, and generalization. They will lead projects focused on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment. Responsibilities include building evaluation pipelines and metrics for driving performance and product readiness, curating and mining data for scenario diversity, and influencing 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 to shape the long-term technical direction of Autonomy.

Skills

Machine Learning

Model Architecture

Data Pipelines

Autonomous Driving

Personalization

Collaboration

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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