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.
The Role
We are looking for a Lead Product Manager – AI/ML to own the strategy and roadmap for SimpliSafe’s AI capabilities layer: the platform, models, and infrastructure that power both our monitoring intelligence stack and our customer-facing AI experiences.
You will own the AI capabilities that make both of those better: the models that detect real threats faster, the pipelines that keep them production-ready, and the platform decisions that determine what SimpliSafe can build at all. Domain PMs on monitoring and consumer will be your peers and primary customers. Your job is to make their roadmaps possible.
You will work directly with a world-class AI/ML engineering team on hard problems: real-time video analysis on edge hardware, trust-critical decision-making where errors have real consequences, and consumer AI experiences that drive value in a recurring revenue business. You will need to hold a sharp product point of view while translating between deeply technical ML constraints and business outcomes.
What You Will Own
1. AI Platform & Capabilities
- Define the multi-year strategy and roadmap for SimpliSafe’s AI/ML capabilities layer: the models, data pipelines, and platform infrastructure that domain teams build on.
- Translate complex product needs from monitoring, platforms, consumer domain, and platform/firmware PMs into crisp, actionable requirements for the AI/ML engineering team.
- Establish the quality bar for model performance in production: define what “good” looks like in terms of precision, recall, latency, and real-world safety outcomes, not just benchmark scores.
- Own platform decisions including model evaluation frameworks, data pipeline architecture, and the trade-offs between prompt engineering, fine-tuning, and third-party model adoption.
- Manage production model health: define processes for detecting and responding to model drift, data quality degradation, and performance regression.
- Build and maintain alignment across product, engineering, data science, design, and monitoring operations on priorities, trade-offs, and release criteria.
- Drive the strategy for AI safety, governance, and compliance: lead product-level privacy, consent, and bias mitigation decisions while partnering with Legal and Privacy teams to establish rigorous policies for biometrics and customer data protection.
2. Monitoring Intelligence Applications
- Partner with the Monitoring Product and Operations teams to define the AI capability requirements for Active Guard and 24/7 professional monitoring; specifically threat detection, triage, and escalation.
- Drive measurable reduction in false alarm rates and response latency. These directly affect customer trust, retention, and cost to serve. Ultimately driving Customer Lifetime Value (CLV).
- Represent AI capability constraints and opportunities clearly to the monitoring team so that roadmap decisions are grounded in what the platform can actually deliver.
- Work with human-in-the-loop workflows to ensure monitoring agents and AI systems operate as an effective team.
- Collaborate closely with the Monitoring and Experiences team to identify 1-2 big, disruptive bets in AI in the home security market.
3. Customer-Facing AI Applications
- Partner with consumer domain PMs to define AI capability requirements for customer-facing features: security insights, anomaly summaries, proactive notifications, and guided self-service.
- Identify where customers are currently underserved or confused where machine learning could reduce friction, increase confidence, and prevent churn.
- Define the interaction model for AI-assisted experiences and ensure capabilities are accessible to a non-technical consumer audience without sacrificing depth.
- Work with design and research to close the loop between customer behavior data and AI product decisions.
What We Are Looking For
Required
- 8+ years of product management experience, with at least 3 years shipping AI or ML-driven features at consumer scale.
- Proven ability to define and champion a multi-year technical product vision that materially reshaped a product or platform.
- Sufficient technical depth to work credibly with AI/ML engineers: you understand model evaluation, data pipelines, inference trade-offs, edge computing constraints, and what “production-ready” means.
- Experience defining quality and success criteria for systems where errors carry real consequences, not just engagement metrics.
- Strong written and verbal communication; able to translate between deeply technical AI/ML concepts and customer-facing or business narratives.
- Comfort operating in ambiguity with incomplete data; you know how to run structured discovery rather than waiting for perfect information.
- Experience acting as a strategic peer in a matrixed organization, driving cross-functional alignment without direct reporting lines.
Strongly Preferred
- Experience owning or contributing to MLOps platforms: model deployment pipelines, evaluation frameworks, monitoring for drift and degradation.
- Experience shipping products in or adjacent to the home security, IoT, or professional monitoring space.
- Familiarity with real-time video analysis, computer vision, or edge hardware deployments.
- Background working with operations teams where product decisions have direct labor and cost implications.
- Experience building or integrating with large-scale data annotation or human-in-the-loop (HITL) systems.
- Experience building customer-facing AI experiences (conversational interfaces, intelligent notifications, or proactive recommendations) at consumer scale.
Preferred
- Familiarity with agentic AI systems: architectures where AI takes autonomous multi-step actions on behalf of users.
What Success Looks Like
In Your First 90 Days
- You have a working understanding of SimpliSafe’s monitoring operations, AI infrastructure, and current customer experience gaps.
- You have interviewed monitoring agents, operations center leadership, engineers, domain PMs, and customers; you can articulate the two or three highest-leverage AI capability bets available to us.
- You have a draft 12-month roadmap for the AI capabilities layer with clear hypotheses and measurable outcomes, socialized with key stakeholders across product, engineering, and operations.
By End of Year One
- At least one AI-driven monitoring capability is in production with measurable business impact (for example, a reduction in false alarm rate or response latency).
- You have shipped customer-facing