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Product Manager, Generative AI Data

Job in Coos Bay, Coos County, Oregon, 97458, USA
Listing for: NVIDIA
Full Time position
Listed on 2026-02-16
Job specializations:
  • IT/Tech
    Data Analyst, Data Science Manager, AI Engineer, Data Scientist
Salary/Wage Range or Industry Benchmark: 224250 USD Yearly USD 224250.00 YEAR
Job Description & How to Apply Below

Employer Industry

Technology

Why consider this job opportunity
  • Salary up to $224,250
  • Opportunity for equity and a comprehensive benefits package
  • Engage in high‑visibility projects that shape industry standards
  • Work in a diverse and inclusive environment that values creativity and innovation
  • Collaborate with top‑tier professionals in the AI and machine learning fields
  • Contribute to responsible AI practices and ethical data usage
What to Expect (Job Responsibilities)
  • Execute the Data Roadmap by managing the feature backlog and delivery for synthetic and curated data streams
  • Coordinate the end‑to‑end lifecycle of data pipelines, including synthetic data generation and human‑in‑the‑loop annotation
  • Implement data quality checks to ensure high‑standard model inputs
  • Act as a bridge between AI researchers and engineers to identify data gaps and provide necessary datasets
  • Define clear product requirements for internal tools used for data collection and augmentation
What is Required (Qualifications)
  • Bachelor’s degree in Computer Science, Data Science, AI/ML, or a related technical field or equivalent experience
  • Minimum of 5 years of experience in product management, data platforms, or ML‑focused roles within a technology company
  • Solid understanding of LLM basics and the machine learning development lifecycle
  • Proven track record in managing sophisticated technical projects and communicating progress to stakeholders
  • Familiarity with how data quality impacts model performance
How to Stand Out (Preferred Qualifications)
  • Hands‑on experience with Python, SQL, or Spark for data analysis
  • Direct experience with labeling tools or synthetic data generation
  • Familiarity with engineering or LLM benchmarking processes

#Technology #AI #Data Management #Product Management #Machine Learning #Diversity In Tech

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