Research Engineer, Post-Training (All Industry Levels)
Develops alignment algorithms, data pipelines, and sampling methods to optimize post-training AI models for performance and efficiency. Requires PhD or equivalent, ML expertise including reinforcement learning and transformers, and production code experience.
About the job
Responsibilities
- Develop alignment algorithms and loss functions to improve data sample efficiency.
- Write data pipelines to process diverse web data into a format models can ingest.
- Identify quality signals to understand our model’s performance in the real world.
- Design sampling algorithms to improve serving efficiency of large generative models.
Requirements
- At least PhD (or equivalent).
- Write clear and clean production-facing and training code.
- Experience working with GPUs (training, serving, debugging).
- Experience with data pipelines and data infrastructure.
- Strong understanding of modern machine learning techniques (reinforcement learning, transformers, etc).
- Track-record of exceptional research or creative applied ML projects.
Nice to Have
- Experience with product experimentation and A/B testing.
- Experience training large models in a distributed setting.
- Familiarity with ML deployment and orchestration (Kubernetes, Docker, cloud).
- Publications in relevant academic journals or conferences in the field of machine learning.
Skills
Reinforcement Learning, Transformers, PyTorch, Gpus, Data Pipelines, Kubernetes, Docker, GCP, Alignment Algorithms, Distributed Training
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