Forward Deployed Engineer

Roboflow

86d • Hybrid

About The Position

We’re hiring Forward Deployed Engineers (FDEs) to be the first technical hands on our new customer’s Roboflow deployments. After our Sales and Solutions Architecture team closes a deal and validates the technical approach, you step in to take it from proof-of-concept to production, the critical 0-to-1 phase where vision meets reality. You’ll embed directly with new Customers, working alongside their engineering and operations teams to build and deploy their first production computer vision workflows on Roboflow. This is the most difficult, important and rewarding phase of the customer journey. Infrastructure is unfamiliar, data pipelines need to be built, edge devices need to be configured, and models need to perform in real-world conditions, not just in a demo or a lab. The FDE role is more than just execution, you are also our eyes and ears in the field. You'll see the gap between what a Customer thinks they want, and what they actually need. That insight is invaluable, and you'll feed it directly back to our Product and Engineering teams, shaping the Roboflow platform and solution offerings. The best FDEs don't just deploy solutions; they surface the problems worth solving next. Once you’ve proven the deployment works in production and the customer’s team is equipped to operate it, you hand off to our Implementation Engineers who scale, optimize, and expand the solution across additional use cases. You’re the tip of the spear, the person who makes the first deployment work, no matter what it takes; the adventurer, the explorer, the builder. Five years from now, FDE at Roboflow on a resume should mean what a few years at Palantir or a FAANG company means. That's the bar we're hiring against.

Requirements

• 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.

Nice To Haves

• 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.
• Background in IoT, embedded systems, or infrastructure engineering alongside hands-on deployment experience.
• You're a tinkerer outside of work. You own a 3D printer, you've got Raspberry Pis running something in your house, you're constantly tweaking your home network or smart home setup. You enjoy figuring out why something doesn't work as much as you enjoy building it in the first place.

Responsibilities

• 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.

Benefits

• $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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed