About the Company
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and 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, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They 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.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g., Seaborn, Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
Responsibilities
- Ship code that matters. Fix bugs, build features, and create automation that improves the experience for every Modal user — not just the one who reported the issue.
- Work directly with customers. Help developers and ML engineers debug, optimize, and architect their workloads across Slack, email, and calls.
- Build scalable systems. Design tooling, dashboards, and automated workflows that make support efficient at scale — delighting customers at the most important moments.
- Close the feedback loop. Translate patterns you see in the field into concrete improvements — docs fixes, API changes, or new feature proposals.
- Contribute to open source and technical content. Write examples, build demos, and publish content that helps the broader community succeed on Modal.
Requirements
- Accomplished in key areas. You bring depth in either low-level infrastructure or ML/AI, and you're not lost in the other.
- Low-level infrastructure experience. Operating systems, file systems, networking, performance profiling, cluster management and distributed systems.
- AI/ML engineering experience. Training models, optimizing inference, working with GPUs, or building ML infrastructure.
- Automation mindset. Your instinct when you see a manual process is to eliminate it and you have the engineering background to make that happen.
- Clear communicator. Can explain a systems issue to a customer, write a crisp bug report, and draft documentation, all while collaborating internally to ship improvements.