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Scale AI provides data annotation, model evaluation, and deployment infrastructure for building production AI systems. The stack reveals a dual engineering focus: frontend-heavy React/TypeScript for the data-labeling platform, paired with PyTorch/transformers/vLLM for LLM evaluation and serving. Active hiring is heavily skewed toward engineering (162 roles) with a distinct research arm (33 roles), signaling investment in novel evaluation benchmarks and foundation models—work evident in projects building LLM evaluation frameworks and agent-performance assessment tools.
Tech Stack 133 technologies
Core StackReact Python Node.js Next.js TypeScript MongoDB AWS NumPy pandas JavaScript C++ Go Rust Docker Kubernetes Terraform PyTorch TensorFlow Tailwind CSS GCP vLLM SGLang TensorRT-LLM Jax LangGraph CUDA transformers Flash Attention SQL Azure+101 more
AdoptingRAG
What Scale AI Is Building
◆Challenges
- Data integrations
- Scalability challenges
- Scaling infrastructure for millions of tasks
- Undefined requirements
- Strict security & regulatory compliance requirements
- Bias mitigation and model robustness
- Secure ai deployment
- Compliance with security mandates
- Meeting customer commitments in a timely manner
- Scaling ai applications
▲Active Projects
- Custom ai applications
- Ml training pipelines
- End-to-end data integrations
- Foundation platform for ml research and development
- Evaluation frameworks for agent performance and business impact
- Develop novel evaluation benchmarks for large language models
- Scalable backend systems for federal customers
- Implement scalable evaluation pipelines
- Custom integrations with customer data environments
- Pipeline analytics for forecasting bookings
Hiring Activity
Steady340 roles · 100 in 30d
Department
Seniority
Notable leadership hires: Robotics Lead, Chief of Staff, Partnership Lead
Company intelligence
