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Roboflow

SaaS platform for computer vision modeling

Forward Deployed Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • Meaningful experience deploying technology in physical-world environments; breadth across industries, hardware platforms, and deployment contexts is valued.
  • Strong proficiency in Python; experience with systems-level work involving Docker, Kubernetes, networking, and Linux is highly valued.
  • Hands-on experience deploying machine learning or computer vision models to production, with understanding of the difference between a working notebook and a reliable pipeline.
  • Experience with edge computing hardware and constraints, including NVIDIA Jetson, industrial cameras, limited connectivity, and on-premise security requirements.
  • Experience in one or more target verticals: manufacturing, logistics, food processing, automotive, or retail.
  • Willingness to travel approximately 40–50% for on-site customer deployments.

Responsibilities

  • Take validated proof-of-concepts from the pre-sales process and build the first production deployment, including 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 with unpredictable conditions and unreliable connectivity.
  • Work on-site or deeply embedded with customer engineering teams during initial deployment engagements, typically 4–12 weeks; build trust, transfer knowledge, and establish the foundation for long-term success.
  • Write production-grade code for customer environments and address real-world computer vision issues such as lighting variability, camera calibration, model drift, network latency, and edge hardware constraints.
  • Surface the differences between customer requests, actual needs, and machine operators' perspectives, and relay those insights to Product and Engineering teams.
  • Document deployment architecture, create runbooks, and train customer teams to operate the system independently; provide a clean handoff to Implementation Engineers for scaling and expansion.
  • Identify and resolve technical risks early, finding alternative approaches when a customer environment cannot support the planned architecture.
  • Codify repeatable deployment patterns, starter templates, and reusable artifacts to make future deployments faster and more reliable.

Desired Qualifications

  • Experience in professional services, deployment engineering, or forward deployed engineering.
  • Familiarity with computer vision and machine learning operations tooling and practices, including model versioning, monitoring, and retraining pipelines.
  • Deep industrial subject-matter expertise, including knowledge of production-line throughput, defect rates, and inspection points.
  • Background in Internet of Things, embedded systems, or infrastructure engineering alongside hands-on deployment experience.

About the Company

Roboflow

SaaS platform for computer vision modeling

Roboflow provides a cloud platform that streamlines the full computer vision workflow for developers. It lets users upload image and video data, annotate datasets (including model-assisted labeling), apply preprocessing and augmentation, and train models with a single click, then deploy via API or on edge devices. The service uses freemium and subscription pricing with a free public tier and paid plans that add privacy, features, and support, plus usage credits for training, storage, and inference. Its goal is to make computer vision accessible and usable for individual developers and larger teams alike, including many Fortune 100 companies.

Company size
51-200
Company stage
Series B
Total funding
$62.2M
Headquarters
Des Moines, IA
Founded
2020

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