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character.aicharacter.aiUnited States

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.

225k – 400k/yr
RemoteML Engineering

About the role

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 LearningTransformersPyTorchGpusData PipelinesKubernetesDockerGCPAlignment AlgorithmsDistributed Training

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