Machine Learning Engineer
Tasks
- Automate ML lifecycle
- Build ML infrastructure
- Build data engineering workflows
- Collaborate with customers
- Collect training data
- Deploy machine learning models
- Evaluate generative models
- Generate labeled synthetic samples
- Identify data distribution gaps
- Iterate model performance
- Label training data
- Optimize model pruning
- Quantize models
- Scale ML pipeline
- Train large-scale models
- Train machine learning models
- Translate research into production code
Perks/Benefits
- Customer-facing opportunities
- International relocation support
- Published research culture
- Startup environment
- Support for EB visas
- Support for H1B
- Support for OPT
- Visa support
Skills/Tech-stack
AWS | Cloud platform | Computer Vision | Data Engineering | Data labeling | Deep learning | Diffusion Models | Docker | Generative Models | Google Cloud | Google Cloud Platform | Kubernetes | Machine Learning | Model Deployment | Model Evaluation | Model Pruning | Model Quantization | Model Training | Representation Learning | Self-Supervised Learning | Self-supervised | Supervised Learning | Synthetic data | Variational Autoencoders
Education
N/A
Regions
Countries
States
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