Senior Engineering Manager - Adaptive Bench Compute

Job Description The Role The Senior Manager, Software Engineering for Adaptive Bench Compute leads the team that delivers scalable, on-demand compute infrastructure for automotive test and simulation bench environments. This role owns the day-to-day technical direction, roadmap execution, reliability, and customer enablement of a multi-site platform spanning bench orchestration, scheduling, identity, inventory, job execution, observability, and results management. The Senior manager translates test-program needs into durable platform capabilities, establishes measurable operating outcomes, and builds a high-performing team that can support expanding hardware-in-the-loop workloads across GM. Success requires strong technical judgment, disciplined execution, and the ability to align peer engineering teams around shared outcomes.

What You'll Do

• Lead and develop the approximately 15-person engineering organization across platform services, infrastructure, bench integration, scheduling, developer tooling, and test enablement; establish the operating model to scale from a single-lab MVP to a multi-site operation with hundreds of benches running automation 24/7.
• Own capacity and investment planning across cloud, hardware, networking, licensing, observability, and suppliers. Make transparent investment decisions for multi-location labs hosting millions of dollars in bench assets and define critical observability metrics; establish run-rate spend and cost-per-bench-hour baselines.
• Establish measurable targets for availability, throughput, latency, utilization, cost, capacity, and customer enablement, and use data-driven diagnostics to drive continuous improvement.
• Own the multi-year roadmap and coordinate divisional stakeholders, IT and Networking, and lab operations to deliver capabilities beyond 500 benches, including support for new physical variants and virtual scenarios.
• Direct end-to-end platform delivery across bench registration, inventory, scheduling, job execution, boot, results, artifacts, APIs, and developer tooling; ensure reliable operation across multiple sites and environments.
• Establish clear KSIs and KPIs, measured and reviewed regularly, covering onboarding, automation throughput, availability, queue time, bench utilization, data capacity, cost per bench-hour, and support responsiveness—and use them to drive significant business impact, target millions in avoided manual-test costs, and expedited software delivery.
• Set the technical and operational bar for a high-throughput platform handling thousands of daily workflows and terabytes of evidence, with disciplined architecture, security, testing, observability, resilience, capacity planning, and production-readiness practices.
• Remove systemic blockers across network reachability, site readiness, identity, bench capacity, storage, data movement, and shared infrastructure; make timely tradeoffs across teams to keep delivery and operations moving.
• Recruit, coach, and retain engineers while building leadership depth, succession, and operating mechanisms that reduce reliance on individual heroics.

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