Machine Learning Engineer
Tasks
- Build and own cloud data pipelines
- Build scalable phenomics pipelines
- Collaborate with cross-functional stakeholders
- Deploy mobile first AI products
- Design develop deploy production computer vision models
- Develop automated image preprocessing and quality control
- Drive ML model lifecycle and monitoring
- Orchestrate ML and data workflows
- Productionalize research code into services
- Translate objectives into ML solutions
Perks/Benefits
- 401k match
- Corporate discounts
- Education assistance
- Flexible work options
- Maternity leave
- Medical/Dental/Vision
- Paid Holidays
- Paid vacation
- Paternity leave
- Profit sharing
- Retirement savings contribution
- Wellness programs
- Work-life balance
Skills/Tech-stack
AWS | Active Learning | Airflow | Argo | Azure | Batch Processing | CI/CD | Computer Vision | Containerization | Convolutional Neural Networks | Data Pipelines | Data Validation | Data Warehousing | Deep learning | Docker | ELT | ETL | GCP | Image Processing | Infrastructure as Code | Keras | Kubeflow | MLOps | MLflow | Message Queues | Mobile AI | Model Monitoring | Multimodal Learning | Neural Networks | Object Detection | Object storage | Prefect | Pruning | PyTorch | Python | Quantization | SQL | Scikit-learn | Segmentation | Self-Supervised Learning | Self-supervised | Streaming | Supervised Learning | TensorFlow | Transfer Learning | Vision Transformer | Weights & Biases | XGBoost | “as-code”
Education
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