Senior Machine Learning Data Curation Engineer
Summary
Develops and optimizes machine learning algorithms for autonomous driving lane fusion, combining 2D and 3D perception data to create accurate road representations in real time.
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Dataset Lifecycle Management: Oversee the collection, organizing, cleaning, and maintenance of large-scale, high-quality datasets for model training.
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Pipeline Development: Build and maintain scalable data processing pipelines and automated intelligent agents to continuously ingest, clean, and enrich training data.
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Quality & Benchmarking: Define, track, and optimize dataset quality metrics (e.g., diversity, absence of bias) to directly improve ML model performance.
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Annotation & Labeling: Design and manage data annotation workflows, collaborating with domain experts to ensure clear, accurate classification protocols.
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Governance & Compliance: Maintain data provenance, ensure compliance with data governance policies (e.g., GDPR, HIPAA if applicable), and enforce data security measures.
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Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a highly quantitative field.
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Technical Skills:
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Proficiency in programming languages like Python or SQL.
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Experience with Big Data tools and cloud platforms (e.g., AWS, GCP, BigQuery).
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Familiarity with ML frameworks (e.g., PyTorch, Hugging Face).
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Experience: 3+ years managing large-scale datasets, developing data curation heuristics, and working alongside ML researchers or data scientists.
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Analytical Mindset: Strong problem-solving skills to identify data quality anomalies, address model biases, and establish evaluation frameworks.
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A fun, supportive and engaging environment.
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Infrastructures and computational resources to support your work.
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Opportunity to work on cutting edge technologies with the top talents in the field.
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Opportunity to make a significant impact on the transportation revolution by the means of advancing autonomous driving.
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Competitive compensation package.
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Snacks, lunches, dinners, and fun activities.