Principal Engineer, Model Dev Platform at Wayve

Sunnyvale, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Autonomous Driving, AI, AutomotiveIndustries

Requirements

Expertise in distributed systems, large-scale compute, and system design
Experience with web applications, ML Ops, data pipelines (e.g., Spark-based), and optimization algorithms (e.g., linear programming, heuristic optimization)
Ability to lead technical architecture across front-end web UIs, distributed training, and experiment scheduling
Strong hands-on problem-solving skills for complex, cross-domain challenges
Capability to define and drive engineering standards for performance, latency, availability, reliability, observability, and scalability

Responsibilities

Own the end-to-end architecture of the AI model lifecycle, from raw data ingestion to model training, experiment scheduling, and on-road testing
Design and evolve the overarching architecture of the model development platform, ensuring system-wide reliability, observability, and scalability; define key performance, latency, and availability targets
Partner with the Head of Model Dev Platform to define and execute the technical vision, aligning infrastructure and tooling with company-wide goals
Lead by technical example, diving deep into complex challenges across web applications, distributed compute orchestration, ML Ops, data pipelines, and optimization algorithms
Provide architectural insight and mentorship to empower teams in delivering platform capabilities
Work across disciplines to unify platform architecture and ensure interoperability between systems (e.g., front-end UIs, distributed training, Spark-based data pipelines, experiment scheduling)
Dive deep into technical challenges for subteams, conduct architectural reviews, and propose pragmatic solutions balancing innovation and operational simplicity
Develop and refine experimentation and scheduling systems, optimizing for simulation and on-road testing while balancing hardware availability, safety, and research priorities using algorithmic techniques

Skills

Machine Learning

Distributed Systems

Model Training

Data Ingestion

Experiment Scheduling

Scalability

Platform Architecture

Robotics

Embodied AI

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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