Machine Learning Data Engineer
San Francisco or Remote
Labelbox’s mission is to build the best products for humans to advance artificial intelligence. As a ML Data Specialist, you will work across engineering, customer, and data labeling teams to create highly accurate datasets. This is a unique, cross functional position where you will act as a project/program manager ensuring successful data labeling and machine learning outcomes for customers. You are a creative problem solver with attention to detail.
Current Labelbox customers include American Family Insurance, Lytx, Airbus, Genius Sports, Keeptruckin and more. Labelbox is venture backed by Andreessen Horowitz, Gradient Ventures (Google’s AI-focused venture fund), Kleiner Perkins and First Round Capital and has been featured in Tech Crunch, Web Summit and Forbes.
- Build the world’s first data warehouse solution tailored towards increasing ML development velocity.
- Help ML engineers around the world make sense of their model performance.
- Contribute to a team culture obsessed with MLOps.
- Design and optimize ETL pipelines + ML experimentation tooling
- Deliver on large scale features that impact customers using data-hungry ML in production
- Build a low-debt future for machine learning infrastructure by adhering to MLOps principles
- Contribute enhancements to an API used by numerous ML teams
- Ability to thrive in a dynamic, fast paced startup environment
- Enjoys the intricacies of big data
- Bachelors or Masters in CS preferred or equivalent experience
- Excellent developer with experience building production-scale data pipelines
- Clear history of delivering on data warehousing solutions
Nice to Have
- Intimate experience with MLOps principles (Terraform, CI/CD, experiment management, data management)
- Intimate experience with ETL pipelines (Dataflow, Spark, Beam, Airflow, etc.)
- Intimate experience with data warehousing design (BigQuery, Cassandra, etc.)
Job tags: AI Airflow Big Data BigQuery Cassandra Data Warehousing Engineering ETL Machine Learning ML Spark