Forward Deployed Engineer
The posting does not state a salary. This range is our estimate from the role, the location and the stack — treat it as a guide, not an offer.
- Level
- Mid
- Apply from
- United States
About the role
- Own customer-facing technical deployments.
- Embed on-site with customers 40%-50% of the time.
- Build and configure data pipelines and models.
- Write production-grade Python for edge devices.
- Surface field insights to improve the platform.
Skills the posting asks for
- Python
- Docker
- Kubernetes
- Linux
- MLOps
Summarised from the employer’s posting, which is reproduced in full below.
The employer’s full posting
Our client is building the leading platform for computer vision and physical AI. More than one million developers use the platform to manage image data, annotate datasets, train models, and deploy computer vision systems through APIs and edge devices. The company works with 65% of the Fortune 100, particularly across global manufacturing, logistics, automotive, robotics, and other physical industries. Founded in 2019, the approximately 70-person company has raised more than $99 million, including a $77.5 million Series B.
This Forward Deployed Engineer will take customer computer vision deployments from validated proof of concept to reliable production operation. You will embed with strategic customers, build the technical system around their use case, and solve problems that appear only in factories, warehouses, construction sites, and other physical environments.
What you will do:
• Own customer-facing technical deployments from zero-to-one build through adoption, production launch, and post-deployment maintenance.
• Embed on-site with customers approximately 40% to 50% of the time to move validated proofs of concept into production.
• Build and configure data pipelines, edge devices, computer vision models, and supporting infrastructure.
• Write production-grade Python and solve issues involving lighting variability, camera calibration, model drift, networking, and edge-hardware constraints.
• Work across Docker, Kubernetes, Linux, NVIDIA Jetson, industrial cameras, computer vision, MLOps, and edge-computing systems.
• Build trust with customer executives, engineers, and floor operators.
• Surface field insights to Product and Engineering and improve the core platform.
• Document deployment architectures, create runbooks, and hand successful customers to implementation teams.