Base Salary
$181k - $315k/yr
Responsibilities
- Build, profile, and optimize the training and inference framework.
- Post-train state-of-the-art models to define stable post-training recipes.
- Collaborate with ML teams to accelerate research and development.
- Create a next-gen agent training algorithm for multi-agent/multi-tool rollouts.
Requirements
- 1-3 years of LLM training experience in a production environment.
- Passion for system optimization.
- Experience with post-training methods like RLHF/RLVR and algorithms like PPO/GRPO.
- Ability to operate the architecture of modern GPU clusters.
- Experience with multi-node LLM training and inference.
- Strong software engineering skills, proficient in CUDA, PyTorch, and transformers.
- Strong written and verbal communication skills.
- PhD or Masters in Computer Science or a related field.
Benefits
- Comprehensive health, dental, and vision coverage.
- Retirement benefits.
- Learning and development stipend.
- Generous PTO.
- Potential commuter stipend.
Tech Stack
Categories
About Scale AI
Scale’s mission is to develop reliable AI systems for the world’s most important decisions. We provide the high-quality data and full-stack technologies that power the world’s leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. The Scale Generative AI Platform allows customers to build, evaluate, and control advanced AI agents and applications that continuously improve. The Scale Data Engine provides the technology to collect, curate, and annotate high-quality datasets. Through our Scale Labs, we test models with rigorous benchmarks and novel research to ensure breakthroughs translate into systems people can trust. Scale powers the most advanced LLMs and generative models in the world through RLHF, data generation and model evaluation. We work with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force.