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 ML Engineers who want to work at the intersection of deep technical work and direct customer impact. As an ML FDE, you’ll partner with leading AI companies and foundation model labs to help them achieve state-of-the-art performance on their most demanding workloads: LLM serving, model training (SFT, RLHF), audio pipelines, scientific computing, and more. You’ll help teams reach outcomes most engineers can’t on their own.
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 AI stack, and energy for working directly with customers on hard problems.
What You’ll Work On:Work hands-on with companies like Suno, Cognition, and Meta to architect and optimize production AI workloads on Modal
Contribute to open-source projects (members of the team are active contributors to SGLang) and publish technical content that demonstrates Modal’s capabilities across the AI stack
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 performance advantages tangible
You have 2+ years of professional ML engineering experience, ideally with hands-on work in inference optimization, model training, GPU programming, or ML infrastructure
You’re familiar with the serving (e.g., vLLM, SGLang) and training (e.g., slime, verl, TRL) toolchains and can go deep on at least one (you don’t need all of them)
You’re a strong communicator who can go deep on technical architecture with an engineering 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
Side projects, open-source contributions, or published work you’re proud of in ML or systems performance
Skills Required
- Experience working with AI applications
- Understanding of ML/AI infrastructure including model training, inference, and MLOps workflows
- Exceptional presentation and communication skills with ability to explain complex technical concepts
- Strong business acumen and understanding of enterprise buying processes and procurement
- Experience selling or implementing serverless computing, container platforms, or ML infrastructure solutions
- 2+ years professional software engineering experience
- Willing to work in-person in New York City, San Francisco or Stockholm
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.
