Engagement Manager Lead

About the role

The Solutions Engineering team at Domino is responsible for helping the largest, AI-driven organizations build and operate advanced data science and AI solutions at scale. This role involves owning customer programs end-to-end, leading delivery pods, and driving adoption and growth.

Responsibilities

• Own the customer program end-to-end: from contract signature through expansion.
• Partner with account team to ensure every engagement is scoped to be delivered with excellence, bridging what is sold with what can be executed.
• Direct the delivery pod (Forward Deployed Engineers and Integration & Architecture teams) for customer engagements. Put capacity where it creates the most value and keep programs on track across milestones, stakeholder communication, and account health.
• Earn trust at every level: from C-suite sponsors to the data scientists running models in production.
• Lead the room in solutioning conversations: connect how Domino builds, scales, and governs AI to the outcomes customer leadership actually cares about. Operate as a peer, not a vendor.
• Make adoption real: drive rollout, enablement, and change management so what your team ships gets used, not shelved.
• Turn customer engagements into growth in partnership with customer success and sales — proactively uncovering new use cases and expansion opportunities, qualifying them, and routing them back to internal teams to fuel pipeline.
• Be the customer's voice inside Domino: convert field intelligence into product feedback, escalations, and commercial signals that make the broader team smarter.

Requirements

• 4–8 years in an enterprise customer-facing discipline: implementations, consulting, technical program management, or product.
• Caliber and complexity matter more than the title.
• You move before the problem is fully formed: comfortable when the customer can't yet articulate what's wrong and treating ambiguity as the starting point.
• You turn unstructured executive conversations into executable plans: connecting model development, deployment, and governance without losing what actually matters.
• You know enough about how enterprise data science teams build, scale, and govern models to ask the right questions before the customer knows they need to answer them.
• Real delivery ownership across full cycles, not coordination. You hold an execution rhythm, hold a position when things get hard, and don't create bureaucracy to do it.
• You lead a team: direct reports, a cross-functional pod, or a mix of engineers and specialists. You set direction, develop people, and hold accountability without micromanaging.
• You are technically fluent across software architecture, predictive modeling, and agentic systems: able to get hands-on when it matters and learn new domains fast.
• Solutions-oriented by default. You escalate with a recommendation, not just a problem, and reach for AI to do all of it faster and better.

Qualifications

• A background in life sciences, financial services, or a regulated industry where reproducibility, auditability, and compliance are non-negotiable is a meaningful advantage.

Skills

• Strong communication and interpersonal skills.
• Ability to manage multiple priorities and deadlines.
• Experience with project management tools and methodologies.
• Knowledge of data science and AI technologies.
• Ability to work independently and as part of a team.

Benefits

• Annual US base salary range: $250,000—$275,000 USD.
• Additional benefits may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends.

Pay

• Annual US base salary range: $250,000—$275,000 USD.

Schedule

• Full-time.