Staff ML Engineer: Data Curation & Dataset Strategy (Hybrid)

Digitailor; Sunnyvale (CA); Hybrid; USD 190,000 - 270,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Paternity leave

Maternity leave

Retirement/pension plan

Wellbeing programs

Learning & development budgets

Hybrid working model

Job summary

Wayve Technologies, Inc. is seeking a Staff Machine Learning Engineer for Data Curation & Dataset Strategy in Sunnyvale, CA. You will own ingestion-time data scoring, route data to hot, cold, archive, or delete, and partner with teams to define data distributions for model training.

You will push storage-cost savings through practical heuristics and ML-driven filtering, while helping shape company-wide data policy and dataset content strategy for scalable autonomous driving models.

Qualifications

• Strong autonomous-vehicle or robotics background with large-scale data experience.
• Principal hands-on ML engineer who has built data-scoring or filtering models.
• Experience operating at petabyte scale and related engineering challenges.
• Proficiency in Python and Spark with large-scale cloud data experience.
• Strong quantitative intuition to drive practical, measurable decisions.

Responsibilities

• Build and own ingestion-time models that score and route data (hot/cold/archive/delete).
• Collaborate with training teams to shape data distributions for models.
• Drive early storage-cost savings through heuristics and ML-driven filtering.
• Lead data provisioning and provenance features with cross-functional teams.
• Work closely with Corpus and other data-engineering teams to implement policy decisions.

Skills

Autonomous-vehicle experience

Robotics

Python

Spark

Cloud data experience

Data ingestion & quality

Quantitative intuition

Ownership & leadership

Tools

Spark

Job description

Wayve Technologies, Inc. is seeking a Staff Machine Learning Engineer for Data Curation & Dataset Strategy in Sunnyvale, CA. You will own ingestion-time data scoring, route data to hot, cold, archive, or delete, and partner with teams to define data distributions for model training.

You will push storage-cost savings through practical heuristics and ML-driven filtering, while helping shape company-wide data policy and dataset content strategy for scalable autonomous driving models.