ML Research Scientist -Deep Learning & Transformer Architectures
New York, New York, United States of America
USD 150K-200K Senior-level Full Time
Tasks
- Adapt attention mechanisms for financial data
- Build training pipelines in PyTorch
- Conduct ablation studies
- Design decoder only Transformer architecture
- Develop evaluation for next token prediction
- Develop tokenization scheme for financial time series
- Document research methodology and results
- Enable multi GPU parallelism
- Implement KV cache
- Implement Transformer from first principles
- Implement gradient checkpointing
- Implement mixed precision training
- Implement model quantization
- Implement speculative decoding
- Integrate predictions into live trading pipeline
- Optimize inference for low latency deployment
Perks/Benefits
Skills/Tech-stack
Attention Mechanisms | C++ | Decoder Only | Decoder-only Transformer | GPU parallelism | Gradient Checkpointing | Inference Optimization | Information theory | KV cache | Linear Algebra | Mixed Precision | Mixed-precision training | Model Evaluation | Model Quantization | Multi GPU Parallelism | Multi-GPU | Next Token Prediction | Optimization | Positional encoding | Probability theory | PyTorch | Python | Speculative decoding | Tokenization | Transformer
Education
Regions
Countries
States
Cities
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