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 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've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation.
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.
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, containerization, sandboxing, and more, helping them design and ship production infrastructure on Modal’s platform.
The FDE team today includes world-class software engineers, computational scientists, ML 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.
What You’ll Work On: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
Build trusted relationships with technical leaders (CTOs, VPs of Engineering, ML leads) at companies doing frontier AI work
Conduct technical demos, experiments, and proofs of concept that make Modal’s infrastructure advantages tangible
You have 3+ years of professional software engineering experience
You have hands-on experience with cloud platforms (AWS, GCP, Azure), including compute, storage, networking, and container orchestration (Docker, Kubernetes)
You’re familiar with distributed systems architecture, data pipelines, and Infrastructure as Code (Terraform, Pulumi, CloudFormation)
You’re a strong communicator who can go deep on systems architecture with an infrastructure team and clearly articulate tradeoffs to technical leadership
You have a genuine interest in working directly with customers: you find it energizing to understand someone else’s problem and help them solve it
You’re willing to work in person in New York City, San Francisco, or Stockholm
Experience leading large-scale migration efforts, open-source contributions, or side projects you’re proud of
Skills Required
- 5+ years of experience in solutions engineering, sales engineering, or customer-facing technical roles
- Deep hands-on experience with cloud platforms (AWS, GCP, Azure)
- Strong knowledge of containerization technologies (Docker, Kubernetes)
- Experience with databases (SQL/NoSQL), data pipelines, and distributed systems architecture
- Understanding of ML/AI infrastructure challenges
- Familiarity with Infrastructure as Code (Terraform, Pulumi, CloudFormation)
- Exceptional presentation and communication skills
- Proven track record of supporting enterprise software sales cycles ($100K+ ACV)
- Experience selling or implementing serverless computing, container platforms, or ML infrastructure solutions
What We Do
Deploy generative AI models, large-scale batch jobs, job queues, and more on Modal's platform. We help data science and machine learning teams accelerate development, reduce costs, and effortlessly scale workloads across thousands of CPUs and GPUs. Our pay-per-use model ensures you're billed only for actual compute time, down to the CPU cycle. No more wasted resources or idle costs—just efficient, scalable computing power when you need it.
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