Own data ingestion: Build reliable pipelines to normalize and validate customer and synthetic data.
Define data standards: Create schemas, validation checks, and quality metrics for Replica datasets.
Build curation tooling: Implement tools for dataset filtering, versioning, and annotation support.
Enable ML workflows: Generate high-quality data feeds for training and evaluation across ML models.

Requirements

Data engineering experience: Proven experience building scalable data pipelines and tooling.
ML-aware engineering: Understanding of how data is used in model training and evaluation.
3D Foundations: Practical experience with 3D concepts, geometry, and the linear algebra principles underpinning computer vision (e.g., projections, transformations)
Technical skills: Strong Python proficiency and comfort with large datasets.
Collaborative mindset: Experience working closely with ML engineers on data needs.

Benefits

Competitive compensation: A base pay range of $130,000 - $160,000/yr, depending on your skills, qualifications, experience, and location.
Impactful work: The chance to contribute to the advancement of autonomous systems and AI.
Collaborative culture: A dynamic and supportive work environment where your ideas are valued.
Professional growth: Opportunities to learn and develop your skills in a cutting-edge field.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

data ingestiondata pipelinesdata normalizationdata validationdata schemasdata quality metricsdataset filteringdataset versioningPythonlarge datasets

collaborative mindset