AI/ML Research Engineer
Tasks
- Analyze results and training dynamics
- Build neural modeling systems
- Collaborate with application teams for BCI impact
- Create reproducible training pipelines
- Debug training issues at scale
- Design and run training experiments
- Develop model architectures
- Evaluate model performance on decoding benchmarks
- Implement data and model pipeline code
- Launch and monitor training runs
- Perform scaling pretraining and ablation studies
- Prepare and curate neural datasets
- Produce reusable model artifacts
- Train deep learning models
Perks/Benefits
- N/A
Skills/Tech-stack
Ablation Study | Code review | Data Curation | Deep learning | Distributed Training | Experiment design | GPU Training | Gradient Checkpointing | Knowledge Distillation | Machine Learning | Mixed Precision | Mixed-precision training | Model Evaluation | Multi-GPU | Multi-GPU Training | Multi-task Learning | Neural Networks | Pruning | PyTorch | Python | Quantization | Representation Learning | Self-Supervised Learning | Self-supervised | Series modeling | Supervised Learning | Time Series | Time Series Modeling | Transfer Learning | Unit Testing | Version control
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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