Machine Learning Research Engineer
Tasks
- Build training and inference pipelines
- Contribute to prototype to production playbooks
- Define success metrics and evaluation strategies
- Design applied AI solutions
- Develop dataset construction labeling validation tooling
- Document research findings and decisions
- Ensure model behavior aligns with user needs
- Guide AI roadmap based on research insights
- Implement experimentation workflows
- Implement safety fairness reliability evaluations
- Mentor engineers on applied ML and responsible deployment
- Monitor data quality
- Optimize models for accuracy latency throughput cost
- Prototype and evaluate models
- Translate business problems to ML formulations
Perks/Benefits
Skills/Tech-stack
Ablation Studies | Accelerator hardware | Computer Vision | Data Quality | Data quality monitoring | Deep learning | Distributed Training | Experimentation | Inference Pipelines | JAX | Language Processing | Machine Learning | Mixed Precision | Mixed-precision training | Model Alignment | Model Evaluation | Natural Language | Natural Language Processing | Observability | PyTorch | Python | Quality monitoring | Recommendation Systems | Responsible AI | Retrieval-Augmented Generation | Statistical Evaluation | Structured data | Training pipelines
Education
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