AI Agent Engineer

Here at Scout Motors, we're carrying forward the heritage of one of the most iconic American vehicles in history. A vehicle dating back to 1960. One that forged the path for future generations of rugged SUVs and trucks and will do so once again.

But Scout is more than just a brand, it’s a legacy steeped in a culture of exploration, caretaking, and hard work.

The Scout brand is all about respect. Respect for the past and the future by taking an iconic American brand that hasn’t been around for a while, electrifying it, digitizing it, and loading it with American innovation. Respect for communities by creating a company that stands for its people and its customers. Respect for both work and play, with vehicles that are equally at home at a camp site, a job site, or on a Tuesday commute. And respect for our customers by developing two powertrains that meet their requirements — an all-electric powertrain as well as the Harvester™ range extender powertrain which includes a built-in gas-powered generator with an estimated 500 miles of combined range.

At Scout Motors, we empower our talented, inclusive, and entrepreneurial teams to innovate. What makes a Scout employee? Someone who is a visionary and a leader, who seeks new paths and shares lessons learned. A knowledgeable doer who collaborates across the company to build better. A go-getter with unrivaled passion.

Join us at Scout Motors and be part of shaping the future of transportation. If you're ready to drive change and make history, apply now!

What you’ll do

Become part of an iconic brand that is set to revolutionize the electric pick-up truck & rugged SUV marketplace by achieving the following:

• Design, develop and deploy AI agents on Databricks, owning the full lifecycle from prototyping to production – including versioning, monitoring, and maintenance of multiple agents running in parallel 
• Build and maintain robust evaluation frameworks using MLflow's evaluation suite, implementing and customizing judges (built-in, guideline-based, and custom) to systematically assess agent quality across correctness, safety, and business-specific criteria 
• Drive continuous quality improvement of AI agents through structured offline evaluation pipelines (curated datasets, benchmarks) and online production monitoring, leveraging MLflow tracing to understand agent execution patterns 
• Collect, structure and incorporate human feedback from diverse stakeholder groups (engineering, quality, business) into evaluation workflows and agent improvement cycles 
• Collaborate with Data Engineers, IT, and R&D to define data configurations and pipelines that feed AI agents with reliable, high-quality inputs