Forward Deployed Engineer - ML
At a glance
Mid-level Software Engineering role at Modal. New York · full-time · $180,000–$250,000 base.
techroles summary, based on the employer's posting.
What you'll do
- Design and improve customer production workloads on Modal, including LLM serving, model training, audio pipelines and scientific computing
- Test workload designs through technical demonstrations, experiments and proofs of concept for customer teams
- Coordinate with product and sales colleagues, contributing engineering work and product input
- Develop relationships with technical leaders, while sharing technical work through open-source projects and published content
What you bring
- You have at least two years of professional machine learning engineering experience
- You know serving and training tools, with the ability to work deeply in at least one toolchain
- You can discuss system design with engineers and explain technical tradeoffs clearly to senior technical leaders
Who this fits
This role suits an engineer who enjoys tackling difficult AI problems alongside customers and communicating with technical leaders. In-person work is required in New York City, San Francisco or Stockholm.
From the employer
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’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 May Be a Fit If:
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
Nice-to-Have:
Side projects, open-source contributions, or published work you’re proud of in ML or systems performance
Software Engineering work from LinkedIn, X, Reddit and Upwork, in one list.
Each morning you get everything that fits, strongest first.
Software Engineering$180k+
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