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Tech Stack
Tools & technologiesDockerKubernetesLinuxPython
About the role
Key responsibilities & impact- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Codify repeatable deployment patterns, starter templates, and reusable artifacts that make future deployments faster and more reliable.
Requirements
What you’ll need- 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.
Benefits
Comp & perks- $4000/yr Travel Stipend to travel anywhere anytime to work alongside other Roboflowers
- $350/mo Productivity stipend to spend on things that make your work environment more productive, like high-speed internet at home or a co-working space
- $350/mo AI Tools
- $150/mo Team lunch
- $500/one time Home office
- Cover up to 100% of your health insurance costs for you and your partner or family
- Equity in the company so we are all invested in the future of computer vision
ATS Keywords
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Hard Skills & Tools
PythonDockerKubernetesnetworkingLinuxmachine learningcomputer visionedge computingtroubleshootingdebugging
Soft Skills
interpersonal skillstrust buildingcommunicationproblem solvingcollaboration