What forward deployed ai engineers are paid, what to name in the advert, and who you are up against — from the adverts on Artificial Intelligence Jobs, not a survey. Looking for the roles themselves? See Forward Deployed AI Engineer jobs.
The range employers advertised for forward deployed ai engineers on Artificial Intelligence Jobs over the last 12 months, base salary only, across 15 priced adverts. Adverts that state a salary get more applications than those that hide it, and a figure at or above the midpoint is the single cheapest thing you can do for the response.
A fully remote role draws the whole country and can pay a little under the London figure; an anchored one competes locally and cannot.
Building from nothing is worth more to the right person than running what exists, and they will ask which it is.
A strong lead in the team is worth thousands on the offer; without one, expect to pay for that experience directly.
The skills forward deployed ai engineers on Artificial Intelligence Jobs are actually being asked for, by how often they appear in adverts for the role over the last 12 months. Candidates search by these words; an advert that uses them is the one they find.
Three live adverts for the role, as candidates see them. How rivals pitch it is worth a look before you write yours.
PhysicsX
Builds and deploys customer-facing applications powered by physics AI models, working directly with customers, data scientists and ML engineers. Involves rapid prototyping, backend development of FastAPI microservices in Python, and shipping to cloud infrastructure using Kubernetes, Helm, Terraform and AWS/Azure.
FastAPI · Helm · Kubernetes +3
PhysicsX
Builds and deploys customer-facing applications that put physics AI models into production, working directly with customers, data scientists and ML engineers. Involves full-stack work, FastAPI microservices in Python, and cloud deployment with Kubernetes, Terraform and AWS/Azure, plus 3-4 weeks of travel per quarter to customer sites.
FastAPI · Helm · Kubernetes +3
PhysicsX
Build and deploy applications that put physics AI models into customers' hands, working directly with customers, data scientists and ML engineers. The role spans the full stack: FastAPI microservices in Python, cloud deployment with Kubernetes, Helm, Terraform and AWS/Azure, plus rapid prototyping. Expect travel to customer sites in Asia, North America and Europe for 3-4 weeks per quarter.
FastAPI · Helm · Kubernetes +3
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