3 months ago
Responsibilities
- Take validated proof-of-concepts through data pipeline setup, model optimization, edge device configuration, and integration with customer infrastructure for the first production deployment.
- Deploy and operate computer vision systems on edge hardware in unpredictable physical environments with unreliable connectivity.
- Embed with customer engineering and operations teams, typically for 4–12 weeks per engagement, to build trust and transfer knowledge.
- Write production-grade code and address lighting variability, camera calibration, model drift, network latency, and edge hardware constraints.
- Identify technical risks early and develop alternatives when customer environments cannot support the planned architecture.
- Document deployment architecture, create runbooks, train customer teams, and provide handoffs to Implementation Engineers.
- Feed customer insights back to Product and Engineering teams to influence the platform and solution roadmap.
- Create repeatable deployment patterns, starter templates, and reusable artifacts.
Requirements
- Meaningful experience deploying technology in physical-world environments such as factories, warehouses, construction sites, or labs.
- 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.
- Experience with edge computing hardware and constraints, including NVIDIA Jetson, industrial cameras, limited connectivity, and on-premise security requirements.
- Excellent troubleshooting and debugging skills.
- Strong interpersonal skills for working with executives, engineers, and machine operators.
- 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.
- Preferred experience in professional services, deployment engineering, or forward deployed engineering.
- Preferred familiarity with computer vision and MLOps tooling and practices, including model versioning, monitoring, and retraining pipelines.
- Preferred deep industrial subject matter expertise.
- Preferred background in IoT, embedded systems, or infrastructure engineering alongside hands-on deployment experience.
Benefits
- Remote, hub, home, or coworking work options across the US and Europe, with hubs in New York City, San Francisco, and São Paulo.
- Relocation bonus and opportunities to work in person with teammates.
- Flexible daytime schedule with some required synchronous meetings.
- Travel stipend, productivity stipend, AI tools stipend, team lunch stipend, and home-office support.
- Health insurance coverage for the employee and partner or family, up to the stated limit.
- Company equity.
- Customer engagements typically last 4–12 weeks and require approximately 40–50% travel.
Tech Stack
Categories
Forward Deployed
About Roboflow
Roboflow creates software-as-a-service products to make building with computer vision easy. Over 1,000,000 developers use Roboflow to manage image data, annotate and label datasets, apply preprocessing and augmentations, convert annotation file formats, train a computer vision model in one-click, and deploy models via API or to the edge. https://roboflow.com