Staff Machine Learning Engineer, Data Curation & Dataset Strategy

Description

You would help solve a hard problem: the company builds the embodied intelligence that drives real vehicles safely, and the platform a billion machines may run on later.

You would join the datasets team, which owns the driving data the driving model learns from across ingestion, annotation, retrieval and data quality.

Data volume is growing from hundreds of petabytes toward exabyte scale, so deciding what to keep and where is one of the largest levers on model quality and cost.

You would shape the data that model engineers train on across the company.

The company is still defining how it works, so you would help write the playbook rather than follow one.

Responsibilities

• Build and own an ingestion-time model that scores incoming driving data and sends each item to hot, cold, archive or delete storage.
• Work with training teams to set the right data distributions for each model in development.
• Cut storage costs early with practical heuristics, then move toward ML-based filtering.
• Set company-wide data policy with the ingestion, annotation, retrieval, data quality, training, AV and robotics, evaluation and storage teams.
• Lead a small group of software and ML engineers, working hands on with them to build data provisioning and provenance features into the internal dataset platform.
• Define the dataset content strategy: which data and metadata are needed and why, so training teams can select the most valuable data easily.
• Direct data selection toward current model needs, for example sending scenarios where the model is weak at unprotected right turns at traffic lights straight to hot storage.
• Deliver large, measurable storage savings as ingestion grows across the internal fleet and partner and OEM data.
• Move from heuristics to ML-driven filtering, and shape what gets filtered at the vehicle and edge.
• Pair with the implementation engineer on the dataset platform to turn strategy into production systems.

Requirements

• Strong autonomous-vehicle or robotics experience, including work with large-scale driving, sensor or comparable data.
• Principal-level, hands-on ML engineering experience with models that score, select or filter data.
• Experience operating at petabyte scale and handling the engineering that comes with it.
• Strong Python and Spark skills, with large-scale cloud data experience.
• Strong quantitative intuition, used to make practical, measurable decisions.
• Pragmatic and high ownership, able to set direction across several teams without direct authority.
• Bonus: experience in data selection, retention or filtering for model training.
• Bonus: edge or vehicle-side filtering, computer vision or high-volume IoT data.
• Bonus: leading small cross-functional groups.
• You do not need to meet every requirement; candidates who care about autonomy and want to learn are encouraged to apply.

Conditions

Hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg.

Hybrid model: in-person work in dedicated offices, combined with focused time working remotely.

Interview steps: a 30 min recruiter screen, a 1 hour intro 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 alignment.

The format is explained up front and scheduled around your availability.

The company welcomes applicants regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital or partnership status, sexual orientation, gender identity, veteran status or pregnancy, and does not ask about marriage, pregnancy, care responsibilities or disabilities in adverts or interviews.

Benefits

• Salaries benchmarked against the market every year.
• Meaningful equity, sharing in the company's ownership and long-term success.
• Relocation support, and visa sponsorship where applicable.
• Hybrid working with core hours, plus hands-on time in vehicle workshops and labs.
• Learning and development budget for training, conferences and growth.
• Health insurance, dental cover, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships and team socials.