Field Robotics Deployment Engineer

Genesis AI; United States; On-site; USD 120,000 - 150,000; Full time

Job summary

Genesis AI is seeking an experienced deployer to translate customer use cases into reliable robotic deployments. You will work directly with customers to understand operations, assess automation feasibility, and define clear requirements and success metrics.

You'll design end-to-end deployment solutions spanning software, hardware, model capabilities, and tooling, balancing performance with reliability. On-site trials and iterative refinements ensure the system meets acceptance criteria.

Qualifications

• Track record delivering technical solutions in real-world environments.
• Hands-on engineering with Python and Linux.
• Experience integrating hardware and software via APIs or control systems.
• Ability to translate customer requests into concrete technical plans.
• Bias toward field work and deployment over whiteboard demos.
• Willingness to travel to customer sites.

Responsibilities

• Own the technical path from customer use case to deployment.
• Work with customers to define requirements, success metrics, deployment scope.
• Design deployment solutions across software, hardware, model capabilities, tooling, and customer systems.
• Hands-on at customer sites: configure systems, run evaluations, debug failures, iterate until acceptance criteria are met.
• Collaborate with research, robotics engineering, and platform teams to turn field failures into improvements.
• Build reusable tools and deployment playbooks to speed future site onboarding.

Skills

Python

Linux

ROS/ROS 2

APIs & integration

Job description

Genesis AI is seeking an experienced deployer to translate customer use cases into reliable robotic deployments. You will work directly with customers to understand operations, assess automation feasibility, and define clear requirements and success metrics.

You'll design end-to-end deployment solutions spanning software, hardware, model capabilities, and tooling, balancing performance with reliability. On-site trials and iterative refinements ensure the system meets acceptance criteria.