Machine Learning Research Engineer
Tasks
- Build training and inference pipelines
- Contribute to knowledge sharing and prototype to production playbooks
- Design prototype and evaluate applied AI solutions
- Develop dataset tooling and data monitoring
- Develop experimentation workflows with evaluations
- Document research findings and operational characteristics
- Implement safety fairness and reliability evaluations
- Mentor engineers on applied ML and evaluation
- Optimize models for accuracy latency throughput and cost
- Translate business problems into ML formulations
Perks/Benefits
- N/A
Skills/Tech-stack
Ablation Studies | Accelerator hardware | Baselines | Data Quality | Data Validation | Data labeling | Data quality monitoring | Dataset Construction | Deep learning | Distributed Training | Fairness evaluation | Fine Tuning | JAX | Language Models | Language Processing | Large Language Models | Machine Learning | Mixed Precision | Mixed-precision training | Model Evaluation | Natural Language | Natural Language Processing | Observability | PyTorch | Python | Quality monitoring | Recommendation Systems | Reliability evaluation | Retrieval-Augmented Generation | Safety evaluation | Statistical Evaluation | Structured data | Vision
Education
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