Senior Product Manager, Applied AI

Engine is the AI-native platform for intelligent travel. For a decade, Engine has built the proprietary technology and supply behind modern business travel, serving over 40,000 customers and nearly 2 million travelers, along with leading travel and AI companies that build on Engine's infrastructure. One system powers business travel, group travel, human agents, and AI agents.

That work has earned recognition as one of Fast Company's Best Workplaces for Innovators (2025), TravelTech's Reservation Platform of the Year (2026), and one of Built In's Best Places to Work (2020–2026).

We're looking for bold, ambitious people to help redefine how businesses manage and experience travel. Working here, you can expect exponential growth, an ambitious pace, full ownership, and high-caliber colleagues who push you to do the best work of your career. Every hire raises the bar.

Come outpace the ordinary and build the future of travel with us.

We’ve spent the last two years getting ahead in AI and our customers see it. It’s showing up in our product, our pace of innovation, and our gross margin. Now we’re putting centralized firepower behind it.

We’re launching two teams: Applied AI, focused on transforming how work gets done, and AI Platform, building the tools and infrastructure to scale it. Together, they’ll sharpen how we measure impact, deploy tooling, and set expectations across Engine, taking what’s already a competitive advantage and making it our operating advantage.

You'll build the automation that powers how Engine runs behind the scenes. This is internal tooling, not a consumer-facing product. The goal is to make teams across the business dramatically faster and more scalable. As an embedded Product Manager on the Applied AI team, you'll sit inside one of Engine's business functions. You'll find the manual steps that slow the work down and get them automated. Here's what you'll own:

• Map the work: Shadow the people doing the work and map each step, handoff and wait, so you know where the time goes.
• Pick the workload: Take on one workload at a time, starting with the steps that are repetitive, high-volume and the most time-consuming. You and the functional leader decide what goes next, and you say no to the rest.
• Lock the metric: Before anything gets built, get the functional leader to sign off on a cycle time or unit metric, and measure the baseline.
• Build and ship: Work with engineering to design and ship automation that replaces manual work at Engine's scale, which includes hundreds of thousands of support cases. Use AI tools and agents to speed up your own analysis, prototyping and documentation.
• Scope and roll out: Define the first version you can ship, who uses it first and how it reaches the rest of the function.
• Hand it off: Define what it looks like for the function to run the workload without you, with a named owner inside the function.
• Make the kill call: If the metric doesn't move, come back with a fix or a decision to stop, with a deadline.

We're looking for someone who's ready to make an impact and grow alongside us:

• Experience: 4+ years in product management, ideally on internal tools, operations or workflow products, with at least one AI or automation product in production.
• Process instincts: You've mapped a messy business process, found the steps that cost the most time and removed them.
• Operator comfort: You're happy spending your first weeks shadowing support agents or accountants, and you judge success by whether the work got faster.
• Measurement: You can baseline a cycle time, define a metric a business leader will sign off on, and account for the costs, including model spend and engineering time.
• AI fluency: You understand what LLMs and agents do well, where they fail, and how evals and human review fit into a workflow.
• Influence: You earn trust with leaders who don't report to you, and you hold the line on scope when everyone wants something different.

• The function's key workflows are mapped, showing where the time goes.
• The first workload is chosen, and its metric is signed off and baselined before the build starts.
• The first version is live with real users, and cycle time is dropping.
• What you've learned has gone into the embed playbook that the next embedded PM will start from.

Applications for this role will be accepted through 12/20/2026 until the role is filled. We encourage you to apply early, as we may begin reviewing applications before the deadline.