# Forward Deployed Engineer

**Company:** [Roboflow](https://hotfix.jobs/companies/roboflow)
**Location:** Remote
**Role:** Solutions Architecture
**Skills:** Python, Docker, Kubernetes, Linux, Computer Vision, Machine Learning, Edge Computing, Nvidia Jetson, MLOps, Networking
**Posted:** 2026-05-18

> Forward Deployed Engineers embed with new customers to take validated computer vision PoCs into first production deployments on Roboflow, handling edge hardware, data pipelines, and real-world integration challenges. They surface field insights to shape the product and hand off to implementation teams after successful launch.

## Job Description

## Role and 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.
- **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).
- **Production Engineering**: Write production-grade code that will live in the customer’s environment. Handle real-world computer vision challenges like lighting variability, camera calibration, model drift, network latency, and edge hardware constraints.
- **Be Our Eyes and Ears in the Field**: Surface gaps between customer needs and actual requirements; feed insights back to Product and Engineering teams.
- **Knowledge Transfer & Handoff**: Document deployment architecture, create runbooks, train the customer’s team, and provide clean handoff to Implementation Engineers.
- **De-risk New Deployments**: Identify and resolve technical risks early; shape the Deployment Playbook with repeatable patterns and templates.

## The Skillset You’ll Bring
- Meaningful experience deploying technology in physical-world environments (factories, warehouses, construction sites, or labs).
- 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.
- Experience with edge computing hardware and constraints (NVIDIA Jetson, industrial cameras, limited connectivity, on-premise security requirements).
- Excellent troubleshooting and debugging skills.
- Strong interpersonal skills for embedding with customer teams.
- Experience in target verticals: manufacturing, logistics, food processing, automotive, or retail.
- Willingness to travel ~40–50% for on-site customer deployments.

## Preferred Attributes
- Experience in professional services, deployment engineering, or forward deployed engineering.
- Familiarity with CV/MLOps tooling (model versioning, monitoring, retraining pipelines).
- Background in IoT, embedded systems, or infrastructure engineering.
- Hands-on tinkerer mindset (e.g., 3D printing, Raspberry Pi, home automation).

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