AI Research Engineer
Tasks
- Analyze research results and communicate findings
- Build reusable machine learning research code
- Conduct ablation studies and adversarial testing
- Create data driven research workflows
- Design and execute AI research experiments
- Design and optimize LLM evaluation pipelines
- Develop LLM evaluation protocols
- Develop scalable AI research automation and documentation
- Improve internal benchmarks and evaluation metrics
Perks/Benefits
Skills/Tech-stack
Ablation Studies | Active Learning | Adversarial Testing | Attention Mechanisms | BPE | Bias analysis | DVC | Data Annotation | Data Processing | Data Versioning | Decoder Architecture | Direct Preference Optimization | Encoder Decoder Architecture | Encoder-decoder | Fine Tuning | Function Calling | GPT | JAX | JSONL | Knowledge Distillation | LLM Evaluation | LLM-as-a-Judge | LakeFS | PPO | Pairwise Comparison | Positional Embeddings | Preference optimization | PyTorch | Python | RLHF | React | Reference Free Metrics | Reinforcement Learning | Semantic Bias Analysis | SentencePiece | Supervised Fine Tuning | TensorFlow | Tokenization | Tool-Calling | Transformer Architectures
Education
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