AI Research Engineer (Applied AI)
Tasks
- Build training and inference pipelines
- Collaborate with ML Platform engineers
- Conduct safety fairness reliability evaluations
- Contribute to prototype to production playbooks
- Design applied AI solutions
- Develop dataset tooling and data quality monitoring
- Document research findings and operational characteristics
- Implement experimentation workflows
- Inform AI roadmap based on research insights
- Mentor engineers on applied ML methodology
- Optimize models for accuracy latency throughput cost
- Translate business problems to ML formulations
Perks/Benefits
Skills/Tech-stack
Accelerators | Computer Vision | Data Modeling | Data Quality | Data Validation | Data labeling | Data quality monitoring | Dataset Construction | Deep learning | Distributed Training | Fairness evaluation | Fine Tuning | Inference pipeline | JAX | Language Models | Language Processing | Large Language Models | Machine Learning | Mixed Precision | Mixed-precision training | Model Evaluation | Model Observability | Natural Language | Natural Language Processing | PyTorch | Python | Quality monitoring | Recommender Systems | Reliability evaluation | Responsible AI | Retrieval-Augmented Generation | Safety evaluation | Statistical Evaluation | Training Pipeline
Education
Roles
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