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Active13mo ago

Forward Deployed Engineer - Systems

Modal·📍 New York, United States

Employment

FULL TIME

Work mode

On-site

Experience

MID LEVEL

Salary

$180K – $240K/yr

Pay vs market · Below median

This job's pay ($210,000 a year at the midpoint) is higher than about 35% of 22 salary-listed Forward Deployed Engineer jobs in New York in the last 12 months; their median is $227,500.

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Role overview

Job description

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 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. 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, 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 May Be a Fit If: 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 Nice-to-Have: Experience leading large-scale migration efforts, open-source contributions, or side projects you’re proud of

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About this listing

  • Source: Modal's official careers site · still listed there on 10 Nov 2026
  • Posted 23 Sept 2025

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What they are looking for

Skills & requirements

Department · Engineering Department

Hiring company

Modal

Technology

Cloud infrastructure for teams that develop, train, and serve AI applications at scale.

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