Forward Deployed Engineer
New
R
RaydarComputer Vision, AI
United States, Aligned with U.S. daytime hoursFull-TimeMiddle
Salary$144,000 to $200,000 base salary and $180,000 to $250,000 on-target earnings with uncapped variable compensation, plus competitive equity.
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Job Details
- Experience
- 1 to 8 years
- Required Skills
- DockerPythonIoTKubernetesLinuxMLOpsComputer VisionNetworking
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.
Responsibilities
- 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.
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