Forward Deployed Engineer

This is a fully remote position, open to applicants in California, +1 more state.

📋 Description

• Transform validated proof-of-concepts from the pre-sales phase into the initial production deployment. This entails setting up data pipelines, optimizing models, configuring edge devices, and integrating with the customer's infrastructure.

• Implement and manage computer vision systems on edge hardware within physical settings, where conditions may be unpredictable and connectivity can be inconsistent. This role involves hands-on, hardware-intensive tasks.

• Collaborate on-site or closely with the customer’s engineering team during the initial deployment phase (usually 4–12 weeks per engagement). Establish trust, transfer knowledge, and lay the groundwork for sustainable success.

• Develop production-quality code that will operate in the customer’s environment. Navigate the complex realities of real-world computer vision, including lighting variability, camera calibration, model drift, network latency, and edge hardware limitations.

• You will have closer insights into the customer's genuine challenges than anyone else at Roboflow. You will bridge the gap between what the customer articulates they want, what they truly need, and the perspectives of the machine operators on the floor. This feedback loop to Product and Engineering significantly influences our future developments, representing one of the most valuable contributions FDEs can make to the organization.

• Document your deployment architecture, create operational runbooks, and train the customer’s team to enable independent system operation. Ensure a smooth transition to Roboflow’s Implementation Engineers for scaling and expansion efforts.

• Proactively identify and mitigate technical risks early in the process. If the customer’s environment cannot accommodate the proposed architecture, you will discover alternatives before it escalates into a project failure.

• Codify repeatable deployment patterns, starter templates, and reusable artifacts to enhance the speed and reliability of future deployments.

⛳️ Requirements

• Significant experience deploying technology in real-world environments. We prioritize breadth, having worked across various industries, hardware platforms, and deployment settings, rather than deep specialization at a single company. The most successful FDEs we have recruited have prior experience in factories, warehouses, construction sites, or labs before engaging with vision models.

• Strong expertise in Python; familiarity with systems-level tasks (Docker, Kubernetes, networking, Linux) is highly desirable.

• Practical experience in deploying machine learning or computer vision models into production; you appreciate the distinction between a functioning notebook and a dependable pipeline.

• Familiarity with edge computing hardware and constraints (NVIDIA Jetson, industrial cameras, limited connectivity, on-premise security requirements).

• Outstanding troubleshooting and debugging capabilities; you excel at determining why something operates in a staging environment but fails in production.

• Strong interpersonal abilities - you will be integrated with customer teams and need to foster trust quickly while navigating their internal dynamics. You will engage with executives, engineers, and machine operators, sometimes within the same meeting.

• Experience in one or more of Roboflow’s target sectors: manufacturing, logistics, food processing, automotive, or retail.

• Willingness to travel approximately 40–50% for on-site customer deployments.

🏝️ Benefits

• $4000/yr Travel Stipend to travel anywhere anytime to work alongside other Roboflowers

• $350/mo Productivity stipend to spend on items that enhance your work environment, such as high-speed internet at home or a co-working space

• $350/mo AI Tools

• $150/mo Team lunch

• $500/one-time Home office setup

• Coverage for up to 100% of your health insurance costs for you and your partner or family

• Equity in the company, ensuring we are all invested in the future of computer vision