Forward Deployed Engineer

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).