# Research Engineer, Post-Training (All Industry Levels)

**Company:** [character.ai](https://hotfix.jobs/companies/characterai)
**Location:** Remote
**Role:** ML Engineering
**Salary:** $225k – $400k/yr
**Skills:** Reinforcement Learning, Transformers, PyTorch, Gpus, Data Pipelines, Kubernetes, Docker, GCP, Alignment Algorithms, Distributed Training
**Posted:** 2024-01-24

> 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.

## Job Description

## 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.

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