Machine Learning Engineer, Offline Infrastructure (Entry-Level / New Grad PhD)
Tasks
- Build data pipelines for training datasets
- Evolve offline ML platform architecture at scale
- Improve dataset validation monitoring and testing
- Optimize performance and efficiency of data processing and training
- Support distributed model training workflows
- Support experimentation and model iteration
- Use workflow orchestration tools for ML pipelines
Perks/Benefits
- Commute subsidy
- Comprehensive health insurance
- Employee assistance program
- Employee resource groups
- Employee stock ownership
- Generous vacation and personal days
- Life and disability insurance
- Mental health & wellbeing programs
- Office snacks
- Parental leave
- Retirement and pension plans
- Training and development programs
- Volunteering and donation matching
Skills/Tech-stack
Airflow | Data Pipelines | Data Processing | Distributed Systems | Flyte | Large Scale Data | Large-scale | Large-scale Data Processing | Machine Learning | PyTorch | Python | Ray | Spark | TensorFlow
Education
Regions
Countries
States
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