Overview
I’ve helped design and deliver autonomous systems at Ford, Argo AI, Toyota Research Institute (TRI), and HERE Technologies — contributing as both an engineering and program leader. Along the way, I’ve worked with some of the most respected names in autonomy, built systems across simulation and real-world testing, and helped shape organizations from research groups into production-ready teams.
🏢 Ford Motor Company
Roles:
Engineering Manager, Decision-Making and Path Planning
Program Lead, High-Tech Organizational Development
Engineering Leadership
Led the Decision-Making and Path Planning team within Ford’s Autonomous Vehicle (AV) program. Managed a team of 7 engineers and collaborated with embedded UX, systems, and production software engineers to form a broader autonomy integration team. Worked closely with perception, data, control, simulation, and hardware teams to define and deliver core autonomy features.
Cross-Functional Org Transformation
Helped lead the transformation from an academically rooted R&D team into a production-minded engineering organization. Embedded systems and production engineers throughout the team to support requirement tracking, test traceability, and program-wide integration as a step toward cross-functional teams that could land end-to-end features rather than isolated domain outputs.
Played a key role in the creation of Ford’s High-Tech, High-Demand rotational program, built to attract and develop top-tier AV, AI, ML, and data talent. Helped champion new job classifications and compensation structures, enabling Ford to retain technical talent without relying on inflated management titles or mismatched org structures. Also contributed to the organizational shift from a research-focused AV group into a production-aligned engineering team, helping shape the resources, structure, and planning cadence needed to align AV feature development with Ford’s broader vehicle program roadmap.
🏢 Argo AI
Roles:
Program Lead, Road to the Road (R2R)
Program Lead, Autonomy Integration
Program Lead, Infrastructure
Overview
After Ford’s $1B investment into Argo AI in 2017, I transitioned with the autonomy team as part of that spinout. I was initially the Engineering Manager for the Decision-Making and Path Planning team at Ford, and I was invited by Argo’s co-founders—Brett, Bryan, and Pete—to move into Technical Program Management. They saw my generalist background, cross-functional instincts, and ability to unify engineering efforts across domains. Their foresight shaped the next 8 years of my career, as I embraced program leadership roles that delivered velocity, clarity, and scale.
I led three major programs during my time at Argo:
Road to the Road (R2R)
While Argo (in a way) spun out from Ford, it was mostly starting from scratch. We merged perspectives from Ford, Waymo, and Uber ATG to architect a clean-slate AV stack with modern design principles, full Argo-owned IP, and a roadmap to on-road deployment.
I led the cross-functional program to stand up this new stack, validate hardware design decisions, build out operational processes, and get vehicles safely operating on public roads in multiple states. This included launching a new test track in Michigan, building a compliance framework, and creating a system-wide feedback loop that integrated autonomy, controls, simulation, infrastructure, vehicle systems, and operations.
We made it to public roads in two states within the first year by building fast, reviewing every major design decision, and trusting cross-functional teams to learn and iterate at pace.
Autonomy Integration
After the initial launch, I shifted into leading the Autonomy Integration effort—the daily operating rhythm that brought together perception, planning, mapping, labeling, and infrastructure into a coherent system.
Each evening at 5pm, I ran the integration standup to surface roadblocks, coordinate feature timelines, and track down persistent bugs. We created focused debug teams to resolve critical blockers. Some problems were addressed overnight, others took a long weekend, and some took months to resolve. We stayed on them.
The heart of the work was balancing short-term fixes (like map tweaks) with long-term generalization (like training new model capabilities). That tradeoff—speed versus robustness—shaped our daily decisions and system behavior. We emphasized tracking open issues, regression test results, and feature landings. The integration loop became the scaffolding for system-wide velocity.
Infrastructure
Once autonomy operations were stable, I led the Infrastructure program—the engineering backbone that kept us moving. Working alongside engineering leads, I helped drive:
- Jenkins-based CI/CD pipelines with per-branch validation, daily release gates, automatic rollback, and health dashboards
- Daily and fleet-wide release orchestration with regression tracking
- Automated log snippet generation and tagging for triage and debugging
- Data ingest optimization to prioritize metadata and reduce bandwidth
- OTA updates and post-incident analysis systems
- Slackbot and dashboard integrations to make system status accessible across roles
This work wasn’t headline-grabbing, but it was critical. It gave us observability, speed, and reliability. Without it, we couldn’t have shipped daily or learned fast.
Reflection on Ford-Argo Experience
My time at Ford and Argo taught me to lead cross-functional programs at the edge of speed and complexity. I saw how the same team could double its output under the right structure. We paired deep researchers with seasoned production engineers, created tools to reduce friction, and aligned goals with clarity.
I learned how to manage system-level tradeoffs. Sometimes you fix the map. Sometimes you fix the model. You can’t always do both at once—so you need systems that let you learn which is which.
And I saw a different kind of leadership. One that shows up where the heat is. That makes space for smart teams to work. That takes responsibility. That moves the system forward.
🏢 Toyota Research Institute (TRI)
Roles:
Technical Program Manager, Perception (functional autonomy org)
Technical Program Manager, Vehicle Capability (first cross-functional E2E autonomy program)
TPM Lead, Cross-Functional Programs (player-coach for the Olympics Capability Showcase)
After several years at Ford and Argo AI, I joined TRI to work alongside my former advisor Ryan Eustice and Gill Pratt on Toyota’s next-generation autonomy stack. We focused on two primary efforts: Chauffeur (Level 4 AV) and Guardian (Toyota’s ADAS initiative).
I was drawn by the technical ambition, the global stage of the 2020 Tokyo Olympics, and the opportunity to contribute to what became the Olympics Capability Showcase (OCS)—a program name we settled on shortly after I joined to reflect its ambition and technical significance.
Perception and Early Integration
I joined as TPM for the Perception org and helped improve communication across perception, simulation, autonomy, data, AI/ML, and platform teams. The autonomy stack needed to accelerate, and I brought structure and alignment to feature delivery.
One early example: I helped the team align on a long-delayed sensor suite decision for the Olympic demo vehicle. The topic was technically and politically sensitive, but we clarified program priorities, surfaced trade-offs, and resolved the decision—unblocking multiple workstreams in the process.
Vehicle Capability and TPM Org Leadership
I helped lead the org-wide transition from siloed functional teams to cross-functional, program-driven structures. We launched new delivery cadences, OKRs, sprint planning, and retrospectives. I was assigned the Vehicle Capability program and also began managing the broader TPM team.
We built a program structure that gave teams real scope ownership and let engineers and TPMs lead together. We also launched a project-portfolio proposal system to give more people the opportunity to shape roadmaps beyond the org chart.
Stakeholder Alignment and Global Complexity
TRI’s autonomy programs sat at the intersection of Toyota Motor Corporation, TRI-AD (Japan), Woven Planet, Tokyo government, the International Olympic Committee, and U.S. state regulators in California and Michigan.
Coordinating readiness across those groups meant navigating different cultural assumptions, aligning incentives, and managing both technical and human complexity.
Reflection on TRI
We made enormous progress, but in the end, our public demo didn’t happen. COVID delayed the Olympics and limited spectators, and a separate AV incident during the Paralympics further constrained the company.
I spent nearly two years helping build that system, and while it never took the global stage, I’m proud of what we built. The TPM team I helped shape still exists. The velocity we built was real. And the lessons I took forward—about transparency, leadership, and cultural alignment—have stayed with me.
🏢 HERE Technologies
Role:
Director of Product Technical Program Management
After TRI, I joined HERE Technologies to focus on HD and ADAS map delivery at global scale. My motivation was twofold: to work with trusted colleagues from Ford and Argo, and to apply my autonomy experience in a production context where real customers needed high-quality data delivered daily.
Product-Aligned TPM Leadership
I led a team of 6–8 Technical Program Managers responsible for end-to-end delivery of HD and ADAS mapping products. These efforts supported both legacy platforms and the rollout of UniMap (Universal Map)—a single integrated pipeline designed to ingest, validate, and serve map content to a wide range of customers and vehicle platforms.
Bayesian Prioritization and Data Curation
One of my key contributions was leading the effort to bring Bayesian reasoning to two major areas:
- A SLAM-inspired approach to sensor fusion for map generation. Rather than stacking redundant inputs, we modeled confidence and surprise to identify truly novel environmental information.
- A probabilistic framework for data ingestion prioritization. We assessed incoming metadata to determine the value of processing or discarding it. This helped optimize storage and compute cost without sacrificing fidelity in critical or changing regions.
These approaches reduced overhead and improved system responsiveness. I initiated and led both efforts.
Reflections on HERE
HERE was the most globally distributed engineering org I’ve worked in—spanning the U.S., Germany, India, Denmark, Ukraine, Japan, and more. Meetings were hard to schedule, but cultural fluency was baked in.
I learned that at global scale, the biggest risks are not bandwidth or latency, but coordination and prioritization. Every customer had slightly different needs. Some countries imposed tight data sovereignty laws. Others paid for faster updates or higher fidelity.
The biggest lesson? You can’t map the world perfectly. It’s too big. The goal is a system that learns what matters—and lets the rest go.
Final Thoughts
I’ve led and collaborated across multiple stages of the AV technology lifecycle—from early prototypes and academic systems to safety-critical commercial roadmaps. This journey has taken me across test tracks, international demos, and daily releases, always focused on clarity, coordination, and scalable autonomy.