Forward Deployed Engineer - ML (London)

Carbon Data Solutions; Greater London; Hybrid; GBP 90,000 - 140,000; Full time

Job summary

Modal is seeking Forward Deployed ML Engineers to work at the intersection of deep technical work and direct customer impact in London. You will partner with leading AI teams to optimize production AI workloads, focusing on LLM serving, model training, and high-performance inference.

You’ll join a team of world-class engineers and researchers, collaborating with customers and internal teams to deliver tangible performance improvements and scalable ML infrastructure across multiple industries.

Qualifications

• 2+ years of professional ML engineering experience.
• Strong ability to communicate complex technical topics to stakeholders.
• Experience with inference optimization, model training, and GPU programming.
• Familiarity with ML infrastructure and end-to-end AI workloads.

Responsibilities

• Collaborate with customers to architect and optimize production AI workloads on Modal.
• Contribute to open-source projects and publish technical content showcasing Modal’s capabilities.
• Work with product and sales teams to align engineering with business goals.
• Build trusted relationships with CTOs, ML leads, and engineering leaders at customer companies.
• Conduct demos and proofs of concept to demonstrate performance advantages.

Skills

ML engineering

Communication

GPU programming

Model training

Tools

vLLM

SGLang

slime

verl

TRL

Job description

Forward Deployed Engineer - ML (London) at Modal, via carbon.talent

About Us: AI needs a new infrastructure layer.

London Full-time

Stack

About this role

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 London

Nice-to-Have:

Side projects, open‑source contributions, or published work you’re proud of in ML or systems performance