AI Research Engineer (Applied AI)
Tasks
- Build dataset construction and labeling tooling
- Build training and inference pipelines
- Collaborate with ML Platform engineers
- Contribute to prototype to production playbooks
- Design applied AI solutions
- Document research findings and design decisions
- Evaluate models with baselines and ablations
- Implement experimentation workflows
- Implement safety fairness reliability evaluations
- Mentor engineers on applied ML methods
- Optimize models for accuracy latency throughput cost
- Translate business problems into ML formulations
Perks/Benefits
Skills/Tech-stack
Ablation Studies | Accelerator hardware | Agentic Systems | Computer Vision | Data Modeling | Data Quality | Data quality monitoring | Deep learning | Distributed Training | Experimentation | Fine Tuning | JAX | Language Models | Language Processing | Large Language Models | Machine Learning | Mixed Precision | Mixed-precision training | Model Evaluation | Model Monitoring | Multimodal Machine Learning | Natural Language | Natural Language Processing | Observability | PyTorch | Python | Quality monitoring | Recommendation Systems | Responsible AI | Retrieval-Augmented Generation | Statistical Analysis
Education
Roles
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