Engineering Manager - Datasets Enrichment at Wayve

London, England, United Kingdom

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Not SpecifiedCompensation

Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Autonomous Driving, Artificial Intelligence, AutomotiveIndustries

Requirements

2+ years of engineering management experience in data, ML, or perception domains
Proven experience designing and scaling ML or data processing systems in production
Strong systems thinking across latency, throughput, observability, and robustness
Technical fluency in Python and/or modern data stack (e.g. Spark, Ray, Flyte)

Responsibilities

Lead, grow, and mentor a team of 5–8 engineers across ML, perception, and data domains
Define and scale data enrichment pipelines, including semantic segmentation and cuboid annotation, computer vision scenario understanding, ML-in-the-loop systems (e.g., model-assisted triage)
Set and drive quality and throughput goals for all enrichment outputs
Drive architecture decisions that ensure robustness, efficiency, and scalability
Partner with infra and platform teams to productionize enrichment systems
Define and monitor data quality metrics; lead efforts to improve and enforce quality at scale
Collaborate with teams across autonomy, research, simulation, and data QA to align on deliverables and priorities
Act as a senior technical and organizational leader during a phase of rapid team and system scaling

Skills

ML Pipelines

Data Pipelines

Semantic Segmentation

Cuboid Annotation

Behavior Labeling

Enrichment Workflows

Perception

Datasets

Engineering Management

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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