Senior ML Research Engineer
Tasks
- Build data pipelines for text code and semi structured data
- Collaborate with security researchers to create training and evaluation data
- Create offline evaluation metrics for LLM quality
- Design data definition and curation strategy
- Develop instruction tuning and preference learning pipelines
- Expose models through defined interfaces to product teams
- Implement automated evaluation suites and regression tests
- Implement synthetic data generation and augmentation
- Optimize inference performance and cost with quantization distillation and caching
- Own end to end lifecycle of large language models
- Perform red teaming evaluations
- Scale distributed training and inference on multi GPU clusters
- Train and fine tune LLMs and seq2seq models
Perks/Benefits
- N/A
Skills/Tech-stack
Airflow | Amazon SageMaker | Apache Beam | Argo Workflows | Attention Mechanisms | CI/CD | Caching | Continual pretraining | Contrastive Learning | Dask | Data Deduplication | Data labeling | DeepSpeed | Distributed Training | FSDP | Instruction Tuning | Knowledge Distillation | Kubeflow | LoRA | MLflow | PEFT | Positional encoding | Preference Learning | PyTorch | Python | Quantization | Spark | Synthetic data | TensorFlow | Tokenization | Transformers | Weights and Biases
Education
N/A
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