Sr. Product Manager (HW/AI)

BusPatrol•Austin, TX

About The Position

At BusPatrol, we’re on a mission to make school transportation safer for children and communities. As a leader in AI-powered safety solutions, we bring cutting-edge technology—AI, machine learning, and IoT—to school buses across North America. We’re the most trusted stop-arm safety program globally, delivering transparency and safety at scale. In short, this is purpose driven work that measurably changes behavior in the communities we serve. The Opportunity: If you’re a product leader who thrives at the intersection of AI, edge computing, and mobile connectivity, this is your opportunity to shape the future of automated safety technology. We are seeking an experienced Senior Product Manager to lead our AI and Hardware Product Teams, a new strategic role unifying AI model development and hardware enablement into a single value engine. Reporting to the VP of Product and Strategy, you’ll define the roadmap for BusPatrol’s next-generation AI-at-the-edge platform where hardware, AI, and automation work seamlessly to deliver measurable business outcomes. Align AI and Hardware roadmaps under a single strategic vision. Focus on the optimal AI opportunities and priorities and when not to use AI. Continuously improve models to deliver faster, more efficient, and more cost-effective safety outcomes. Lead the industry with an AI-at-the-edge platform where hardware, AI, and automation work seamlessly Drive speed to market Key Outcomes: This role is critical to transforming the AI in-the-cloud product by extending to our AI at-the-edge for our internal and external stakeholders (target users). High-level goals for this new capability include: Deliver a unified AI + Hardware roadmap (in Jira) tied to business value and performance KPIs. Launch BusPatrol’s new AI Edge model and hardware pilot in April 2026. Partner with the teams to optimize AVA at the edge to reduce our data transmission costs and latency. Improve model performance through rapid training, iteration, feedback loops, and data lifecycle excellence. Build alignment between Edge HW, Firmware, AI, and Cloud teams to accelerate time-to-market. Prepare us to be ready for new use cases and verticals. Own the AI and Hardware Domain Space Own the vision, strategy, and roadmap that unites AI and hardware innovation Partner to define measurable success criteria for AI models and edge deployment performance Prioritize AI investments and model improvement cycles tied to business outcomes Collaborate with firmware, IoT, and platform engineering teams to optimize model performance at the edge Align OTA update cycles, connectivity decisions, and manufacturing timelines with the AI roadmap Balance cost, compute, and performance tradeoffs for on-device inference Foster Cross-Functional Collaboration Act as the connective tissue between AI SMEs, firmware engineers, data teams, and business stakeholders Partner closely with Product, Research, Engineering, Legal, and Operations to drive alignment, foster shared understanding, and ensure the delivery of high-quality, timely outcomes Translate complex AI concepts into clear, business-aligned narratives for executives Drive AI Product Excellence Build and operationalize an AI product framework that links model metrics (precision, recall, latency) to user and business impact Establish repeatable processes for AI lifecycle management, data quality, retraining, and edge deployment Foster experimentation with rigor—combining speed, governance, and validation Champion Governance and Safety Ensure AI models meet privacy, auditability, and regulatory standards (e.g., COPPA, GDPR, evidentiary integrity) Partner with legal and compliance teams to embed AI governance into the product lifecycle Drive transparency and explainability in AI systems Maximize model training while also adhering to increasing data privacy and retention requirements

Requirements

7+ years of experience in product management, with expertise in software, AI, and hardware domains.
Experience in automated traffic enforcement, AI, cameras, and fleet safety technology.
A bias for action, the experience to lead from the front, and the wisdom to empower others while leading from the back.
Proven ability to lead cross-functional strategic initiatives with senior executives.
Proven success leading AI/ML-enabled products, from ideation through deployment
Working fluency in model development concepts (training, validation, metrics, drift management)
Understanding of edge computing, firmware dependencies, and OTA update cycles
Ability to communicate complex AI tradeoffs clearly to executives and engineers alike
A track record of building new capabilities, teams, or frameworks from the ground up.
Strong data science and analytical skills with experience in sizing, Fermi style estimation, impact analysis, metrics, experiments, and measurement.
Comfortable with ambiguity, developing clear and concise priorities and roadmaps.

Responsibilities

Align AI and Hardware roadmaps under a single strategic vision.
Focus on the optimal AI opportunities and priorities and when not to use AI.
Continuously improve models to deliver faster, more efficient, and more cost-effective safety outcomes.
Lead the industry with an AI-at-the-edge platform where hardware, AI, and automation work seamlessly
Drive speed to market
Deliver a unified AI + Hardware roadmap (in Jira) tied to business value and performance KPIs.
Launch BusPatrol’s new AI Edge model and hardware pilot in April 2026.
Partner with the teams to optimize AVA at the edge to reduce our data transmission costs and latency.
Improve model performance through rapid training, iteration, feedback loops, and data lifecycle excellence.
Build alignment between Edge HW, Firmware, AI, and Cloud teams to accelerate time-to-market.
Prepare us to be ready for new use cases and verticals.
Own the vision, strategy, and roadmap that unites AI and hardware innovation
Partner to define measurable success criteria for AI models and edge deployment performance
Prioritize AI investments and model improvement cycles tied to business outcomes
Collaborate with firmware, IoT, and platform engineering teams to optimize model performance at the edge
Align OTA update cycles, connectivity decisions, and manufacturing timelines with the AI roadmap
Balance cost, compute, and performance tradeoffs for on-device inference
Act as the connective tissue between AI SMEs, firmware engineers, data teams, and business stakeholders
Partner closely with Product, Research, Engineering, Legal, and Operations to drive alignment, foster shared understanding, and ensure the delivery of high-quality, timely outcomes
Translate complex AI concepts into clear, business-aligned narratives for executives
Build and operationalize an AI product framework that links model metrics (precision, recall, latency) to user and business impact
Establish repeatable processes for AI lifecycle management, data quality, retraining, and edge deployment
Foster experimentation with rigor—combining speed, governance, and validation
Ensure AI models meet privacy, auditability, and regulatory standards (e.g., COPPA, GDPR, evidentiary integrity)
Partner with legal and compliance teams to embed AI governance into the product lifecycle
Drive transparency and explainability in AI systems
Maximize model training while also adhering to increasing data privacy and retention requirements