Architect, Training & Compute at Wayve

Sunnyvale, California, United States

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Not SpecifiedCompensation

Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Autonomous Vehicles, Artificial Intelligence, AutomotiveIndustries

Requirements

10+ years designing and building large-scale distributed systems, with at least 4 years focused on GPU-based cloud infrastructure
Proven experience enabling large-scale AI training, inference, or computer vision workloads in GPU clusters
Deep understanding of petabyte-scale data architecture, including storage federation, high-throughput access, and data locality for AI workloads
Strong technical leadership with a track record of defining and communicating architectural strategy, balancing long-term vision with delivery needs
A natural mentor with a history of developing engineers and influencing technical direction across teams
Advanced degree in Computer Science, Electrical Engineering, or a related field—or equivalent industry experience
Experience with multi-cloud orchestration (desirable)

Responsibilities

Define and evolve the architecture for how Wayve allocates and orchestrates training and inference workloads across thousands of GPUs and multiple data centers, ensuring optimal throughput, resiliency, and cost efficiency (Global Compute Strategy)
Design systems that enable fast, reliable access to high-volume sensor and simulation data across geographies, ensuring the right data is always available for training, evaluation, and inference, and preparing Wayve for exabyte-scale (Petabyte-Scale Data Federation)
Build the foundations that enable large-scale AI workloads to run seamlessly across hybrid and multi-cloud environments (Cross-Region GPU Job Execution)
Act as a trusted partner to leadership in aligning compute investments and architecture with company strategy, growth plans, and performance goals (Cloud Infrastructure Advisory)
Uplift the broader engineering org through architectural coaching, technical deep dives, and by cultivating a culture of operational and engineering excellence (Technical Leadership & Mentorship)

Skills

AI Infrastructure

Compute Systems

Storage Systems

Cloud Infrastructure

Model Training

Model Deployment

Embodied AI

Foundation Models

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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