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.
