Role Overview:
The Engagement Manager will lead a forward deployment engineering POD embedded within a medical device client environment. This role is both technical and commercial, operating at the intersection of video-based machine learning, data-driven use case discovery, and customer-facing program execution. The Engagement Manager will work directly with the client's sales organization and their end customers, often on-premises, to identify, shape, and deliver high-impact video data monetization opportunities.

Act as the primary point of contact between the client's sales channel, end customers, and the deployed engineering POD.
Lead discovery sessions, on-site observations, and requirements gathering for video-based machine learning opportunities.
Translate observed workflows and customer challenges into structured AI/ML use cases.
Work with data scientists and ML engineers to scope model development, data preparation, and validation activities.
Drive the full lifecycle of engagement execution-from opportunity identification through delivery and customer success.
Ensure technical alignment, risk management, and timeline adherence across cross-functional teams.
Shape and present commercial proposals, ROI analyses, solution roadmaps, and value narratives.
Manage stakeholder communication, reporting, and executive updates.
Ensure compliance with healthcare/medical device regulatory standards when working with sensitive video data.
Promote a culture of experimentation, rapid iteration, and hypothesis-driven development within the POD.

8+ years of experience in customer-facing delivery, technical program management, or AI/ML engagement leadership.
Strong understanding of machine learning concepts, especially video analytics, computer vision, and supervised learning workflows.
Experience working in healthcare, medical devices, or regulated environments involving sensitive data.
Ability to translate ambiguous customer needs into well-scoped technical requirements.
Excellent communication, storytelling, and client management skills.
Proven ability to work on-premises with customers, driving hands-on discovery and solution design.
Strong commercial acumen with experience in pricing, proposal creation, and value-modeling.
Ability to lead cross-functional engineering teams in a dynamic, field-deployed setting.

Background in data science, computer vision, or ML engineering.
Prior experience in forward deployment engineering, field engineering, or consulting.
Knowledge of FDA/medical device regulatory frameworks.
Familiarity with video annotation tools, model training pipelines, and edge-deployment workflows.

Consistent delivery of high-quality, high-impact AI/ML use cases.
Strong relationships with client sales teams and end customers.
Increased customer adoption, satisfaction, and revenue from video data solutions.
Efficient coordination of the engineering POD with clear outcomes and minimal friction.