Senior ML Research Engineer
Tasks
- Apply instruction tuning and preference learning
- Build LLM evaluation and experimentation frameworks
- Build LLM training pipelines
- Develop model interfaces for product teams
- Generate synthetic training data
- Implement data curation and labeling pipelines
- Optimize inference performance and cost
- Run automated evaluation suites and red teaming
- Scale distributed model training and inference
- Train and fine-tune large language models
Perks/Benefits
- N/A
Skills/Tech-stack
Airflow | Amazon SageMaker | Apache Beam | Argo | Attention Mechanism | CI/CD | Continual pretraining | Contrastive Learning | Dask | DeepSpeed | Distributed Training | Embeddings | Experiment tracking | FSDP | Instruction Tuning | Knowledge Distillation | Kubeflow | LoRA | MLflow | PEFT | Preference optimization | PyTorch | Python | Quantization | RAG | Retrieval-Augmented Generation | Spark | TensorFlow | Tokenization | Tokenizer | Transformers | Vector Databases | Weights and Biases
Education
N/A
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