Principal Data Engineer, Data Quality & Datasets Pipelines

Description

The company builds embodied AI that drives real vehicles safely, with the aim of making autonomy available across many vehicle types and markets.

The Datasets team owns the driving data the AI learns from. That covers ingestion, automatic and manual annotation, retrieval and data quality.

The data set is growing from hundreds of petabytes toward exabyte scale, and not all of it is equally useful for training. The team decides what is kept and where, and builds the systems that carry those decisions out.

This role sits in the Datasets team, working as a pair with the dataset strategy lead, who designs the models, and closely with the data infrastructure team.

The company is still defining many of its ways of working, so this role suits someone who wants to shape the process and take real ownership.

Responsibilities

• Build and iterate filtering, retention and offload policies across hot, cold, archive and delete storage.
• Build the data movement and pipelines that the pre-ingestion selection model relies on.
• Decide where and how selection models run, weighing compute cost against the value of the data.
• Create early, low-compute decision points that stop low-value data from going through costly standardisation and enrichment.
• Work with the storage and data infrastructure teams, and monitor data quality so the datasets match what model training needs.
• Deliver large, measurable storage cost savings through early, low-compute decisions.
• Make sure the datasets cover the most useful driving behaviour and environments for training, treating this as a data quality problem as much as an engineering one.

Requirements

• You are a senior data or software engineer who has run data ingestion or data quality pipelines at petabyte scale.
• You are strong in Python and Spark, with cloud data engineering experience.
• You use good judgement on data filtering and quality, and you know how to check that valuable data has not been discarded.
• You ship practical solutions quickly and are comfortable iterating on live systems.
• You are a software engineer at heart, focused on the systems around models rather than the models themselves.
• You enjoy working closely with data infrastructure and storage teams.
• Bonus, not required: experience in autonomous vehicles, robotics, fleet data or high-volume IoT, or with retention, offload and data lifecycle policies.
• If you do not meet every requirement but care about autonomy and want to learn, you are still welcome to apply.

Conditions

The main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg.

The working model is hybrid, combining in-person work in the office with focused remote work.

The interview process has five stages: a 30 minute recruiter screen, a 1 hour introduction with the hiring manager, 2 hours of competency interviews on system design and technical leadership, a 1 hour domain deep dive, and a 1 hour final interview on mission and values.

The interview format is explained in advance and scheduled around your availability.

The company is committed to a diverse, fair and respectful workplace that is open to everyone, regardless of citizenship and other protected characteristics.

Benefits

• Salaries are benchmarked against the market every year.
• Meaningful equity, so you share in the company's ownership and long-term success.
• Relocation support and visa sponsorship where applicable.
• Hybrid working with core hours, plus hands-on work in vehicle workshops and labs.
• Learning and development budgets for training, conferences and growth.
• Comprehensive benefits, including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, and wellbeing partner discounts.