Principal Product Manager, AI

About SimpliSafe SimpliSafe is a leading innovator in home security, on a mission to make every home a safe home. We build user-centric hardware, software, and services that protect what matters most, with a hybrid work model and a highly collaborative, low-ego culture. We’re embracing a hybrid work model that enables our teams to split their time between office and home. Hybrid for us means we expect our teams to come together in our state-of-the-art office on two core days, typically Tuesday, Wednesday, or Thursday – working together in person and choosing where they work for the remainder of the week. We all benefit from flexibility and get to use the best of both worlds to get our work done. Why are we hiring? Well, we’re growing and thriving. So, we need smart, talented, and humble people who share our values to join us as we disrupt the home security space and relentlessly pursue our mission of keeping Every Home Secure. Why this role exists We’re scaling AI and machine learning across our products, devices, and operations. To do this well, we need a Principal Product Manager who understands not only how to apply AI in user experiences, but also how models, data, and pipelines are built, deployed, and operated in production—including on resource-constrained, edge hardware. You will lead our highest-impact AI initiatives end-to-end: defining where to invest, shaping model and data requirements, partnering with MLOps and ML engineering on deployment (cloud and edge), and driving continuous improvement after launch. What you’ll own Vision strategy Define and communicate the multi-year roadmap for key AI/ML capabilities across SimpliSafe. Identify and prioritize AI opportunities where models and data can materially improve safety, customer experience, or efficiency—on both devices and cloud services. Make build-vs-buy decisions for AI capabilities in partnership with data science and engineering. End-to-end AI systems (cloud edge) Partner with data scientists, ML engineers, and MLOps to design and deliver end-to-end ML solutions—from problem framing through data, training, evaluation, deployment, and monitoring. Work with hardware and embedded teams to shape edge AI/ML experiences (e.g., on-device detection, low-latency decisions, bandwidth-aware designs). Define model-level requirements (metrics, latency, cost, guardrails) and connect them to business outcomes (e.g., false alarm reduction, detection accuracy, handle time, CSAT). Translate product needs into requirements for ML platform capabilities (model serving, observability, experiment tracking, human-in-the-loop tools). Generative AI LLMs Lead product direction for LLM and multimodal use cases (e.g., text, vision, sensor data). Decide when to use prompt engineering, RAG, fine-tuning, or traditional ML—and how to evaluate quality, safety, and hallucinations. Design workflows that incorporate human review and escalation where needed. Execution leadership Drive cross-functional execution across product, engineering, data science, hardware/firmware, operations, and go-to-market. Establish feedback loops (customer feedback, annotation programs, operational input) and use them to guide retraining and iteration. Operate at Principal level: set direction across multiple teams and mentor other PMs. Risk, ethics communication Ensure ethical and responsible use of AI, including privacy, bias, explainability, and safety guardrails. Communicate how AI systems work, their benefits, and their limitations to executives and non-technical stakeholders. What we’re looking for 8+ years of product management experience, including significant ownership of AI/ML or data-intensive products. Clear track record of shipping production ML systems (not just integrating third-party AI APIs), in close partnership with data science, ML engineering, and MLOps. Principal-level impact: leading cross-team initiatives, shaping strategy, and influencing senior stakeholders. ... (truncated, view full listing at source)