Machine Learning Research Engineer
Tasks
- Build training and inference pipelines
- Collaborate to ensure efficient compute storage and accelerator usage
- Design prototype and evaluate applied AI solutions
- Develop dataset construction and monitoring tooling
- Document research findings and operational characteristics
- Implement experimentation workflows with baselines and ablations
- Implement safety fairness and reliability evaluations
- Mentor engineers on applied ML methodology and responsible deployment
- 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 | Agentic Systems | Computer Vision | Data Quality | Data quality monitoring | Deep learning | Distributed Training | Experimentation | Fairness evaluation | Fine Tuning | Inference Pipelines | JAX | Language Models | Language Processing | Large Language Models | Machine Learning | Mixed Precision | Model Evaluation | Multimodal | Natural Language | Natural Language Processing | Orchestration | PyTorch | Python | Quality monitoring | Recommendation Systems | Reliability evaluation | Responsible AI | Retrieval-Augmented Generation | Safety evaluation | Statistical Evaluation | Structured data | Training pipelines
Education
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