Senior ML Research Engineer
Tasks
- Apply instruction tuning and preference learning
- Build data pipelines for text code and logs
- Collaborate with security researchers to create training data
- Define offline evaluation metrics for LLM quality
- Expose models through product ready interfaces
- Generate and augment synthetic data
- Implement automated evaluation and regression tests
- Optimize inference performance with quantization distillation and caching
- Own end to end lifecycle of large language models
- Run red teaming scenarios
- Scale distributed training and inference
- Train and fine-tune transformer models
Perks/Benefits
- N/A
Skills/Tech-stack
Airflow | Amazon SageMaker | Apache Beam | Argo Workflows | Attention Mechanism | Automated Evaluation | BERT | Benchmarks | CI/CD | Caching | Dask | Data Pipelines | DeepSpeed | Distributed Training | Embeddings | Experiment tracking | FSDP | Knowledge Distillation | Kubeflow | Language Models | Large Language Models | MLflow | NLP | Positional encoding | PyTorch | Python | Quantization | RAG | Red Teaming | Regression testing | Retrieval-Augmented Generation | Seq2Seq | Spark | TensorFlow | Testing | Tokenization | Transformers | Vector Databases | Version control | Weights and Biases
Education
N/A
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