Senior Product Manager

Pam is building the AI workforce for automotive retail. Our voice and messaging agents help dealerships answer inbound demand, proactively reach customers, schedule appointments, and turn conversations into measurable business outcomes. Pam serves more than 800 dealership rooftops. As we continue to grow, we are investing in the foundational capabilities that make our agents more intelligent, natural, reliable, and deeply connected to the systems dealerships use every day. About the Role: We are looking for a deeply technical product manager to own three foundational areas of Pam’s product: agent quality, the real-time voice stack, and product integrations. You will define how Pam’s agents are evaluated, monitored, released, and improved in production; how they deliver natural and reliable voice conversations; and how they take action across dealership systems. This is a high-impact role at the intersection of AI, voice, infrastructure, and customer experience. You will work closely with engineering, applied AI, customer success, and go-to-market teams to translate emerging technology and real-world customer needs into dependable product capabilities. The right person combines strong product judgment with enough technical depth to engage directly in model, architecture, voice, reliability, and integration decisions. You understand that a compelling AI demonstration is only the beginning—the product must perform consistently across unpredictable, real-world conversations. What you\'ll own: Define the vision, strategy, and roadmap for how Pam’s agents are evaluated, released, monitored, and improved. Establish evaluation frameworks covering task completion, conversation quality, factual accuracy, policy adherence, latency, and cost. Own the LLMOps product capabilities supporting model routing, prompt and configuration management, experimentation, evaluation, observability, and version control. Define quality standards and release criteria for new models, prompts, tools, and agent capabilities. Build feedback loops that turn production conversations, customer outcomes, and failure analysis into measurable product improvements. Partner with engineering and applied AI on model selection, orchestration, tool use, guardrails, observability, and build-versus-buy decisions. Make agent behavior and performance understandable to both technical and business teams. Own the product strategy and roadmap for Pam’s voice stack, including speech recognition, speech generation, model orchestration, streaming infrastructure, and telephony. Define how Pam handles turn-taking, interruptions, silence, transfers, voicemail, error recovery, and other real-world call behavior. Establish latency and reliability targets across the voice pipeline. Define how voice quality is measured, including response latency, recognition accuracy, interruption handling, call resolution, transfer success, and overall conversation quality. Ensure Pam performs effectively across accents, noisy environments, inconsistent phone networks, complex call flows, and high call volumes. Guide decisions about model selection, vendor partnerships, proprietary capabilities, and voice architecture. Translate production call patterns and edge cases into clear product improvements. Product integrations Own the product capabilities connecting Pam with dealer management systems, CRMs, service schedulers, inventory systems, contact-center infrastructure, and other dealership software. Define reusable APIs, webhooks, authentication patterns, data models, and integration workflows. Improve the speed and reliability with which Pam can launch new integration partners and dealership customers. Establish standards for data freshness, reconciliation, permissions, error handling, and integration monitoring. Work closely with customers and implementation teams to understand workflows and technical requirements. Balance immediate customer needs with investments in reusable capabilities that reduce customer-specific development. Prioritize integrations based on customer impact, commercial opportunity, implementation effort, and strategic value. How you’ll work Develop a cohesive roadmap connecting customer needs, company priorities, technical architecture, and business outcomes. Spend time with dealership groups, implementation teams, frontline operators, and internal users to understand where the product succeeds or breaks down. Translate ambiguous problems into clear requirements, evaluation criteria, sequencing decisions, and measurable outcomes. Work directly with engineers and applied AI practitioners on system design and technical tradeoffs. Lead prioritization across customer needs, reliability improvements, foundational investments, and emerging AI capabilities. Align teams through clear product narratives, requirements, decision documents, and launch plans. Take capabilities from early prototypes through production launch and ongoing improvement. Communicate performance, risks, tradeoffs, and investment priorities to company leadership. What you\'ll bring 5+ years of experience in product management, engineering, applied AI, or an equivalent technical product-building role. Meaningful ownership of a technically complex AI, platform, infrastructure, or developer product. Strong understanding of production AI systems, including evaluation, observability, reliability, latency, and cost. The ability to work directly with engineers on architecture, model choices, APIs, and technical tradeoffs. Experience defining metrics and quality standards for products whose performance cannot be measured by uptime alone. Strong analytical judgment and a demonstrated ability to use data and customer evidence to make product decisions. The ability to translate customer-specific problems into reusable product capabilities. Experience aligning technical, customer-facing, and business stakeholders around shared priorities. Comfort operating in a fast-moving environment where the technology and customer expectations are evolving rapidly. Especially valuable Experience with real-time voice, speech recognition, speech generation, streaming architectures, or telephony. Experience with agent orchestration, tool use, prompt and configuration management, guardrails, model evaluation, or AI observability. Experience building APIs and integrations for SaaS products. An engineering, computer science, applied AI, or machine-learning background. What success looks like Location This is an in-office position based in Pam’s Tysons Corner, Virginia office but we can be open to hybrid (3 days in office). Working together in person enables close collaboration across product, engineering, applied AI, customer success, and company leadership. Compensation The annual base salary range for this position is $150,000–$185,000, plus equity. #J-18808-Ljbffr Pam