Research Engineer – Generative AI and Computer Vision
Tasks
- Build proof-of-concept solutions
- Collaborate with cross-functional teams
- Define evaluation benchmarks and metrics
- Design data efficient learning pipelines
- Detect out of distribution inputs
- Develop computer vision inspection models
- Document findings and communicate results
- Generate synthetic image datasets
- Integrate AI components into scalable platforms
- Monitor deployed models and data drift
- Research generative ai methods
- Run ablation studies and experiments
- Train and evaluate deep learning models
- Transfer research into production ready ai components
Perks/Benefits
- N/A
Skills/Tech-stack
AUC | Active Learning | Adversarial Networks | Anomaly Detection | Anomaly Detection Metrics | Autoencoders | Computer Vision | Convolutional Neural Networks | Data Augmentation | Data Drift | Data Drift Detection | Data Generation | Deep learning | Detection Metrics | Diffusion Models | Domain Adaptation | Domain Randomization | Drift Detection | F1-score | Few-Shot Learning | Few-shot | Generative AI | Generative Adversarial Networks | Hugging Face | Image Generation | Image classification | Image to Image Generation | Image-to-image | Instance Segmentation | Language Models | Machine Learning | Model Monitoring | Neural Networks | NumPy | Object Detection | OpenCV | Out of Distribution | Out-of-distribution Detection | Pandas | Precisión | PyTorch | Python | ROC | Recall | Scikit-learn | Self-Supervised Learning | Self-supervised | Semantic Segmentation | Supervised Learning | Synthetic Data Generation | Synthetic data | Test-Driven | Test-Driven Development | Transfer Learning | Transformers | Uncertainty Estimation | Variational Autoencoders | Version control | Vision Language Models | Vision Transformers | Vision-language
Education
Roles
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