Forward Deployed Engineer - ML

FDE Times; Town of Tonawanda (NY); On-site; USD 120,000 - 190,000; Full time

Job summary

Modal is seeking Forward Deployed ML Engineers to work at the intersection of deep technical work and direct customer impact. As part of the FDE team, you will partner with leading AI companies to optimize state-of-the-art workloads—from LLM serving to model training and data pipelines—directly with customers.

The role emphasizes strong engineering fundamentals, curiosity across the AI stack, and energy for solving hard problems with customers on real-world workloads.

Qualifications

• 2+ years of professional ML engineering experience, including inference optimization or model training.
• Ability to go deep on at least one toolchain.
• Strong communication with engineering teams and leadership.
• Willing to work in-person in NYC, SF, or Stockholm.

Responsibilities

• Work hands-on with customers to architect and optimize production AI workloads on Modal.
• Contribute to open-source projects and publish technical content.
• Collaborate with product and sales as engineer and product stakeholder.
• Build trusted relationships with technical leaders at frontier AI companies.
• Conduct demos, experiments, and proofs-of-concept showing Modal's performance advantages.

Skills

ML engineering

Inference optimization

GPU programming

Tools

vLLM

SGLang

slime

verl

TRL

Job description

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 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 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 with technical leaders (CTOs, 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 interest in working directly with customers — you find it energizing to understand someone else's problem and help them solve it
• Bonus: side projects, open‑source contributions, or published work you're proud of in ML or systems performance
• Willing to work in‑person in New York City, San Francisco, or Stockholm