11 months ago
Base Salary
$150k - $230k/yr
Responsibilities
- Ship bug fixes, features, and automation that improve the experience for Modal users.
- Help developers and ML engineers debug, optimize, and architect workloads through Slack, email, and calls.
- Design tooling, dashboards, and automated workflows that make customer support efficient at scale.
- Translate recurring customer issues into documentation fixes, API changes, and feature proposals.
- Write examples, build demos, and publish technical content while contributing to open source.
Requirements
- Demonstrated depth in either low-level infrastructure or ML/AI, with working familiarity in the other area.
- Experience with operating systems, file systems, networking, performance profiling, cluster management, and distributed systems.
- Experience training models, optimizing inference, working with GPUs, or building ML infrastructure.
- Ability to automate manual processes and build production-quality engineering solutions.
- Strong communication skills for explaining systems issues, writing bug reports and documentation, and collaborating internally.
Tech Stack
Seaborn
Categories
Solutions Engineering
About Modal
Customers rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. Every era of computing came with new workloads that previous infrastructure couldn't serve: mainframes, databases, the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice. The window to build is open right now.