Edge-First Deployment Engineer for Computer Vision

Roboflow; United States; Hybrid; USD 130,000 - 210,000; Full time

Benefits offered by this job

Travel stipend $4000/yr travel stipend

Productivity stipend $350/mo

AI Tools stipend $350/mo

Team lunch $150/mo

Home office $500 one-time

Health insurance coverage for you and/

Equity in the company

Job summary

Roboflow is seeking a Forward Deployed Engineer to be the first technical hand on new customer deployments, taking proof-of-concept to production. You’ll embed with customer teams, build data pipelines, configure edge devices, and ensure deployments perform in real-world conditions.

The role blends hands-on engineering with direct customer collaboration and feedback to shape Roboflow’s platform. You’ll work across edge hardware, ML models, and production systems, travel ~40–50% for on-site

Qualifications

• Meaningful experience deploying technology in physical-world environments. We value breadth, working across different industries, hardware platforms, and deployment contexts, over depth at a single company. The best FDEs we've hired have built things in factories, warehouses, construction sites, or labs before ever touching a vision model.
• Strong proficiency in Python; experience with systems-level work (Docker, Kubernetes, networking, Linux) is highly valued.
• Hands-on experience deploying machine learning or computer vision models to production; you understand the gap between a working notebook and a reliable pipeline.
• Experience with edge computing hardware and constraints (NVIDIA Jetson, industrial cameras, limited connectivity, on-premise security requirements).
• Excellent troubleshooting and debugging skills; you’re the person who figures out why it works in staging but not in production.
• Strong interpersonal skills - you'll be embedded with customer teams and need to build trust quickly while navigating their internal dynamics. You'll talk to executives, engineers, and machine operators, sometimes in the same meeting.
• Experience in one or more of Roboflow’s target verticals: manufacturing, logistics, food processing, automotive, or retail.
• Willingness to travel ~40–50% for on-site customer deployments.

Responsibilities

• 0-to-1 Deployment: Take validated proof-of-concepts from the pre-sales process and build the first production deployment. This includes data pipeline setup, model optimization, edge device configuration, and integration with customer infrastructure.
• Edge-First Engineering: Deploy and operate computer vision systems on edge hardware in physical environments. Where conditions are unpredictable and connectivity is unreliable. This is hands-on, hardware-heavy work.
• Embed with Customers: Work on-site or deeply embedded with the customer’s engineering team during the initial deployment phase (typically 4–12 weeks per engagement). Build trust, transfer knowledge, and establish the foundation for long-term success.
• Production Engineering: Write production-grade code that will live in the customer’s environment. Handle the messy realities of real-world computer vision: lighting variability, camera calibration, model drift, network latency, and edge hardware constraints.
• Be Our Eyes and Ears in the Field: You'll be closer to the customer’s real problems than anyone else at Roboflow. You'll surface the gap between what the customer says they want, what they actually need, and what the machine operators on the floor think. That feedback loop back to Product and Engineering shapes what we build next, and it's one of the most valuable things FDEs bring back to the company.
• Knowledge Transfer & Hand-off: Document your deployment architecture, create runbooks, and train the customer’s team so they can operate the system independently. Provide a clean handoff to Roboflow’s Implementation Engineers for scaling and expansion.
• De-risk New Deployments: Identify and resolve technical risks early. If a customer’s environment won’t support the planned architecture, you find the alternative before it becomes a project failure.
• Shape the Deployment Playbook: Codify repeatable deployment patterns, starter templates, and reusable artifacts that make future deployments faster and more reliable.

Skills

Python

Docker

Kubernetes

Linux

Edge computing

Production deployment

Customer engagement

Problem solving

Tools

NVIDIA Jetson

Industrial cameras

Job description

Roboflow is seeking a Forward Deployed Engineer to be the first technical hand on new customer deployments, taking proof-of-concept to production. You’ll embed with customer teams, build data pipelines, configure edge devices, and ensure deployments perform in real-world conditions.

The role blends hands-on engineering with direct customer collaboration and feedback to shape Roboflow’s platform. You’ll work across edge hardware, ML models, and production systems, travel ~40–50% for on-site