Forward Deployed ML / Inference Engineer jobs

2 open roles. The performance-engineering corner of the role: GPU-level inference optimization, serving, and training work embedded with customers and labs.

Modal

Forward Deployed Engineer, ML

$180K–$250K + equity

$300M+ ARR, 5x growth, a $4.65B Series C — and the FDE team ships open source (they contribute to SGLang) while tuning inference for Suno, Lovable, and Cognition. Only 2 years' ML asked; the job is GPU-level performance work at frontier labs, not slideware.

New York / San Francisco (on-site; Stockholm option)AI infraML performanceGPU

Groq

Forward Deployed AI/ML Engineer

$143.6K–$289.1K

Groq literally frames the job as 'a hands-on AI startup CTO' — deploy and optimize inference on their LPU stack inside customer orgs, up to 50% travel. $143,600–$289,100, and one of the few FDE seats on the chip side of the inference war rather than the model side.

Mountain View / Remote (US)inference infrahands-on CTOtravel

Adaption

Founding Forward Deployed Machine Learning Engineer

closedNot disclosed

A 'Founding' Forward Deployed ML Engineer at a young lab hiring across SF, Singapore, and India at once — founding scope on the customer-facing edge, where you 'operate at the bleeding edge of our technology and our customers' most consequential problems simultaneously.'

San Francisco / Singapore / India (on-site)founding titlethree geosapplied ML

Black Forest Labs

Forward Deployed Machine Learning Engineer

closedNot disclosed

The FLUX team's forward-deployed seat — proof the FDE motion has spread from LLM labs to image gen. Requires actual diffusion-model internals knowledge, which shrinks the candidate pool to near zero.

San Francisco / Freiburg (Germany)image genFLUXdiffusion expertise