over 1 year ago
Base Salary
$201k - $251k/yr
Responsibilities
- Build, profile, and optimize the ML training and inference framework.
- Collaborate with ML teams to accelerate research and development and enable next-generation model and data-curation work.
- Research and integrate state-of-the-art technologies to optimize ML systems.
Requirements
- Experience with multi-node LLM training and inference.
- Experience developing large-scale distributed machine learning systems.
- Strong software engineering skills and proficiency with tools and frameworks such as CUDA, PyTorch, Transformers, and FlashAttention.
- Strong interest in system optimization and effective written and verbal communication skills.
- Ability to work in a cross-functional team environment.
- Expertise in LLM post-training methods or next-generation LLM use cases, including instruction tuning, RLHF, tool use, reasoning, agents, or multimodal systems, is preferred.
Benefits
- Comprehensive health, dental, and vision coverage.
- Retirement benefits, learning and development stipend, generous paid time off, and potentially a commuter stipend.
- Full-time position located in San Francisco, New York, or Seattle.
- Eligible roles may include equity-based compensation.
- Candidates must wait 90 days before reconsideration for the same role.
Tech Stack
Categories
About Scale AI
Scale AI builds data annotation services and AI development tools for enterprises and government agencies, sold as a platform and managed services. Its products include the Scale Generative AI Platform for building and evaluating agents and the Data Engine for collecting, curating, and labeling training data, including RLHF and model evaluation. Founded in 2016 and headquartered in San Francisco, the company is privately held and works across domains from computer vision to LLM applications.