Forward Deployed Engineer

Role and Responsibilities

• Take validated proof-of-concepts from the pre-sales process and build the first production deployment.
  • 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.
  • 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.
• 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.
  • Surface the gap between what the customer says they want, what they actually need, and what the machine operators on the floor think.
• 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.
• Shape the Deployment Playbook.
  • Codify repeatable deployment patterns, starter templates, and reusable artifacts that make future deployments faster and more reliable.

The Skillset You’ll Bring

• Meaningful experience deploying technology in physical-world environments.
  • Breadth, working across different industries, hardware platforms, and deployment contexts, over depth at a single company.
• Strong proficiency in Python; experience with systems-level work (Docker, Kubernetes, networking, Linux) is highly valued.
  • 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.
• Knowledge Transfer & Handoff.
  • Document your deployment architecture, create runbooks, and train the customer’s team so they can operate the system independently.
• De-risk New Deployments.
  • Identify and resolve technical risks early.
• Shape the Deployment Playbook.
  • Codify repeatable deployment patterns, starter templates, and reusable artifacts that make future deployments faster and more reliable.

Preferred Attributes

• Experience in a professional services, deployment engineering, or forward deployed engineering role.
  • Familiarity with CV/MLOps tooling and practices (model versioning, monitoring, retraining pipelines).
• Deep industrial subject matter expertise.
  • The kind of person who can walk onto a production line and immediately start asking the right questions about throughput, defect rates, and inspection points.
• 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.