Modal Labs is a platform that accelerates development, reduces costs, and scales workloads for data science and machine learning teams using generative AI models and serverless GPUs. Our pay-per-use model ensures efficient resource utilization, allowing users to access powerful computing power on demand without idle costs. Modal offers a wide range of services including LLM inference and fine-tuning, generative model training and inference, and computational biology, among others.
Open Positions
AI Field Engineer
5+ years of technical roles. Cloud experience (AWS, GCP, Azure). Strong knowledge of containerization & databases
Support Engineer
3-5 yrs support exp, developer-facing products experience, Python/command line skills, and knowledge base familiarity. Cloud platforms a plus
Member of Technical Staff - Python SDK
5+ years Python dev experience; advanced Python knowledge; strong product sense; excellent communication skills
Controller
CPA with 10+ years accounting/finance experience. Familiarity with US GAAP and systems like QuickBooks/NetSuite. Hands-on experience with large US GAAP compliance
Security Engineer
5+ years in security role. Proficiency in Python, Go, or Rust. Strong fundamentals in systems/networking & cloud security
Technical Writer
3+ years technical writing experience, strong technical background, and ability to explain complex concepts
Member of Technical Staff - Systems
5+ years of experience writing high-quality production code, strong cloud skills, and experience building high-performance distributed systems
Member of Technical Staff - ML Performance
5+ years of high-quality, high-performance code experience, experience with torch, Nvidia GPU architecture, CUDA, and ML performance engineering
Member of Technical Staff - Product (Frontend)
2+ years of software engineering experience, strong product sense, and experience with modern front-end Javascript frameworks
Developer Relations Engineer
3+ years software engineering/ML experience. 1+ year as ML engineer. Strong communication and metric-driven approach
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