Forward Deployed Engineer - ML
AI summary of the role
Forward Deployed ML Engineer partnering with leading AI companies and foundation model labs to optimize production workloads (LLM serving, model training, audio pipelines, scientific computing) on Modal's serverless GPU infrastructure.
What you’ll do
- Work hands-on with companies like Suno, Lovable, Cognition, and Meta to architect and optimize production AI workloads on Modal
- Contribute to open-source projects (e.g., SGLang) and publish technical content demonstrating Modal's capabilities across the AI stack
- Collaborate with Modal's product and sales teams as both an engineer and product stakeholder
- Build trusted relationships with technical leaders (CTOs, VPs of Engineering, ML leads) at frontier AI companies
What you’ll bring
- 2+ years of professional ML engineering experience with hands-on work in inference optimization, model training, GPU programming, or ML infrastructure
- Familiarity with serving (e.g., vLLM, SGLang) and training (e.g., slime, verl, TRL) toolchains — deep on at least one
- Strong communicator who can go deep on technical architecture and articulate tradeoffs to technical leadership
- Genuine interest in working directly with customers to understand and solve their problems
Technologies
vLLM · SGLang · slime · verl · TRL · GPU · LLM serving · model training · inference optimization · GPU programming
About Modal
Serverless cloud infrastructure for AI workloads — instant GPU access, sub-second container startups, native storage for training, batch, and low-latency inference.
Series B
Source and classification
Implementation & delivery · Evidence for this classification:
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 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're helping 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
More from the 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 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 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 m [... source excerpt omitted ...] entists, 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. You will: Work hands-on with companies like Suno, Lovable, 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 wit [... source excerpt omitted ...] , VPs of Engineering, ML leads) at companies doing frontier AI work Conduct technical demos, experiments, and proof-of-concepts that make Modal's performance advantages tangible 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. You don't need all of these, but you should be able to go deep on at least one. Strong communicator who can go deep on technical architecture with an engineering team and clearly articulate tradeoffs to technical leadership Genuine in
Employer postings · Data from · Sources