AI Research Engineer
Tasks
- Build training and inference pipelines
- Conduct model production deployment and continuous improvement
- Define success metrics and evaluation strategies
- Design applied AI solutions
- Develop dataset construction and labeling tooling
- Document research and deployment decisions
- Implement experimentation workflows with baselines and ablations
- Implement safety fairness and reliability evaluations
- Mentor engineers on applied ML methodology
- Monitor data quality
- Optimize models for accuracy latency throughput and cost
- Prototype and evaluate machine learning models
- Translate business problems into ML formulations
Perks/Benefits
- N/A
Skills/Tech-stack
Ablation Studies | Computer Vision | Data Quality | Data quality monitoring | Deep learning | Distributed Training | Experiment design | Inference Pipelines | JAX | Language Processing | Machine Learning | Mixed Precision | Mixed-precision training | Model Alignment | Model Evaluation | Multimodal Learning | Natural Language | Natural Language Processing | Observability | PyTorch | Python | Quality monitoring | Recommendation Systems | Responsible AI | Retrieval-Augmented Generation | Statistics | Training pipelines
Education
Roles
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