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Open nowPosted 44 days ago

Forward Deployed Engineer

Workable (global search)108,016 open roles

Where
United States
Work mode
Remote
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Your applicationOpen nowForward Deployed EngineerWorkable (global search) · United States

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Early applications get read.

7.9% of postings close within 7 days. Measured by our own scanner across the market. Workable (global search) postings stay open a median of 7 days.

Share of postings closed within
  1. 1.6%1 day
  2. 3.6%3 days
  3. 7.9%7 days
  4. 14.9%14 days
  5. 34.0%30 days
This job: posted 44 days ago

Workable (global search) median: 7 days open

The 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.

Requirements

• Approximately 1 to 8 years of experience in forward deployed engineering, field engineering, solutions architecture, customer-facing software engineering, or a closely related role.

• Demonstrated ownership of a full customer-facing technical deployment from initial build through production adoption and maintenance.

• Strong Python software engineering skills and experience shipping production systems.

• Hands-on experience with systems-level work using Docker, Kubernetes, networking, and Linux.

• Experience with computer vision, MLOps, edge computing, robotics, automation, industrial software, IoT, or related physical systems.

• Ability to communicate and build trust with executives, engineers, and front-line operators.

• Highly motivated, coachable, low-ego, eager to learn, responsive to feedback, and collaborative.

• Comfort working independently in ambiguous field environments and taking responsibility for follow-through.

• Willingness and ability to travel approximately 40% to 50% for customer deployments.

• Bachelor's degree in computer science, engineering, or a related technical field.

• Unrestricted authorization to work in the United States. Visa sponsorship and transfers are not available.

Nice to have: experience in manufacturing, logistics, automotive, robotics, automation, NVIDIA Jetson, industrial cameras, model monitoring, retraining pipelines, personal hardware projects, or other hands-on physical-system deployments.

Benefits

$144,000 to $200,000 base salary and $180,000 to $250,000 on-target earnings with uncapped variable compensation, plus competitive equity. Benefits include full family health-insurance coverage, a $4,000 annual travel stipend, a $350 monthly productivity stipend, and a $350 monthly AI-tools budget. Remote-first within the United States, aligned with U.S. daytime hours, with approximately 40% to 50% travel. Midwest cities and major travel hubs are strongly preferred.

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