Senior Machine Learning Data Curation Engineer

XPENG

$174K — $295K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a quantitative field.
  • Proficient in programming languages such as Python and SQL.
  • Experience with Big Data tools and cloud platforms (e.g., AWS, GCP, BigQuery).
  • Familiar with machine learning frameworks like PyTorch and Hugging Face.
  • 3+ years managing large-scale datasets and collaborating with ML researchers.

Responsibilities

  • Oversee collection, organization, cleaning, and maintenance of high-quality datasets for model training.
  • Build and maintain scalable data processing pipelines and automated agents for data ingestion.
  • Define, track, and optimize dataset quality metrics to enhance ML model performance.
  • Design and manage data annotation workflows, collaborating with domain experts for accurate classifications.
  • Ensure compliance with data governance policies and maintain data security measures.

Benefits

  • Supportive and engaging work environment.
  • Access to infrastructures and computational resources for your projects.
  • Opportunity to work with cutting-edge technologies and top industry talents.
  • Significant impact on advancing autonomous driving.
  • Social perks like snacks, lunches, dinners, and fun activities.
Full Job Description
We are seeking a Machine Learning Data Curation Engineer to spearhead the data pipeline development and dataset management for our core AI initiatives. You will bridge the gap between raw data and robust, high-performance machine learning models by designing intelligent tools for data collection, cleaning, and annotation.

Key Responsibilities:
  • Dataset Lifecycle Management: Oversee the collection, organizing, cleaning, and maintenance of large-scale, high-quality datasets for model training.
  • Pipeline Development: Build and maintain scalable data processing pipelines and automated intelligent agents to continuously ingest, clean, and enrich training data.
  • Quality & Benchmarking: Define, track, and optimize dataset quality metrics (e.g., diversity, absence of bias) to directly improve ML model performance.
  • Annotation & Labeling: Design and manage data annotation workflows, collaborating with domain experts to ensure clear, accurate classification protocols.
  • Governance & Compliance: Maintain data provenance, ensure compliance with data governance policies (e.g., GDPR, HIPAA if applicable), and enforce data security measures.


Qualifications:
  • Education: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a highly quantitative field.
  • Technical Skills:
    • Proficiency in programming languages like Python or SQL.
    • Experience with Big Data tools and cloud platforms (e.g., AWS, GCP, BigQuery).
    • Familiarity with ML frameworks (e.g., PyTorch, Hugging Face).
  • Experience: 3+ years managing large-scale datasets, developing data curation heuristics, and working alongside ML researchers or data scientists.
  • Analytical Mindset: Strong problem-solving skills to identify data quality anomalies, address model biases, and establish evaluation frameworks.


What do we provide:
  • A fun, supportive and engaging environment.
  • Infrastructures and computational resources to support your work.
  • Opportunity to work on cutting edge technologies with the top talents in the field.
  • Opportunity to make a significant impact on the transportation revolution by the means of advancing autonomous driving.
  • Competitive compensation package.
  • Snacks, lunches, dinners, and fun activities.


The base salary range for this full-time position is $174,720 - $295,680, in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

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