Forward Deployed Engineer - Systems

About Us: 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. The Role: We're looking for Forward Deployed Engineers on our engineering team who want to work at the intersection of deep infrastructure work and direct customer impact. As an FDE, you'll partner with leading AI companies and foundation labs on cloud architecture, networking, storage, [... source excerpt omitted ...] engineers, and former founders. We're looking for people with strong engineering fundamentals, deep curiosity across the infrastructure stack, and energy for working directly with customers on hard problems. You will: Work hands-on with companies like Suno, Lovable, Cognition, and Meta to architect and deploy massive-scale production workloads on Modal Lead technical discovery and architecture sessions with prospective and existing customers Architect migration paths from existing cloud infrastructure (AWS, GCP, Azure) to Modal's serverless platform Collaborate with Modal's product and sales teams, contributing to the platform as both an engineer and a product stakeholder Bu [... source excerpt omitted ...] Ps of Engineering, ML leads) at companies doing frontier AI work Conduct technical demos, experiments, and proof-of-concepts that make Modal's infrastructure advantages tangible Requirements: 3+ years of professional software engineering experience Hands-on experience with cloud platforms (AWS, GCP, Azure) — compute, storage, networking, and container orchestration (Docker, Kubernetes) Familiarity with distributed systems architecture, data pipelines, and Infrastructure as Code (Terraform, Pulumi, CloudFormation) Strong communicator who can go deep on systems architecture with an infrastructure team and clearly articulate tradeoffs to technical leadership Genuine interest in w