Machine Learning Engineer - Pre-Training at Wayve

London, England, United Kingdom

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Not SpecifiedCompensation

Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Autonomous Driving, AI, AutomotiveIndustries

Requirements

Experience optimizing large scale training jobs on GPU compute clusters
Experience in working in platform teams and working with research teams
Experience in reporting and tracking over time benchmarked performance in an open and accessible way
Ability to write high quality, well-structured and tested Python code
BS or MS in Machine Learning, Computer Science, Engineering, or a related technical discipline or equivalent experience
Desirable
Solid experience working with concurrent, parallel and distributed computing
Experience using Nvidia NSight Systems
Experience implementing GPU kernels
Knowledge of computing fundamentals - what makes code fast, secure and reliable

Responsibilities

Profile training jobs to identify their bottlenecks, e.g. using NVIDIA Nsight Systems
Design and implement efficiency improvements to maximise MFU, e.g. tensor parallelism, model compilation, mixed precision
Design and implement observability tools, e.g. to track MFU
Collaborate closely with Research teams to integrate training efficiency improvements and create a culture of performance optimization

Skills

Key technologies and capabilities for this role

Machine LearningPre-TrainingNVIDIA Nsight SystemsTraining OptimizationLarge Scale TrainingProfilingEmbodied AIFoundation ModelsPyTorch

Questions & Answers

Common questions about this position

What are the essential skills required for this Machine Learning Engineer role?

Essential skills include experience optimizing large scale training jobs on GPU compute clusters, working in platform teams with research teams, reporting and tracking benchmarked performance openly, and writing high-quality Python code. A BS or MS in Machine Learning, Computer Science, Engineering, or related field is also required.

What are the key responsibilities for this position?

Key responsibilities include profiling training jobs to identify bottlenecks using tools like NVIDIA Nsight Systems, designing efficiency improvements to maximize MFU such as tensor parallelism and mixed precision, implementing observability tools to track MFU, and collaborating with Research teams to integrate improvements.

What is the company culture like at Wayve?

Wayve fosters a diverse, fair, respectful, and inclusive culture that values unique skills and perspectives, embraces uncertainty and complex challenges, aims high while staying humble, and backs team members to deliver impact in a fast-paced environment.

What salary or compensation does this role offer?

This information is not specified in the job description.

Is this role remote or does it require office work?

This information is not specified in the job description.

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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