Modal
Forward Deployed Engineer - ML
New York
Sponsorship not specifiedDetected 149 days ago
Machine LearningLLMsValuationLeadership
About the role
- We're looking for Forward Deployed ML Engineers who want to work at the intersection of deep technical work and direct customer impact.
- 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.
Responsibilities
- Work hands-on with companies like Suno, Lovable, Cognition, and Meta to architect and optimize production AI workloads on Modal
- 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
- You're helping teams reach outcomes most engineers can't on their own.
Requirements
- 2+ years of professional ML engineering experience, ideally with hands-on work in inference optimization, model training, GPU programming, or ML infrastructure
- Familiarity with the serving (e.g., vLLM, SGLang) and training (e.g., slime, verl, TRL) toolchains.
Nice to have
- Willing to work in-person in New York City, San Francisco, or Stockholm
Company info
- 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 https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, 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 https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
- 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 https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, 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 https://modal.com/blog/modal-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 https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
This listing is sourced directly from Modal's careers page and normalized into a canonical job model.