Senior Product Manager, SafeRide Health

• 7+ years of product management experience, including 3+ years building ML/AI-powered products or platforms (consumer or enterprise)
• Proven track record of shipping end-to-end experiences that improved core metrics, with accountability for KPIs and business outcomes
• Strong technical fluency with modern AI/ML stacks (LLMs, prompt engineering, RAG, embeddings/vector databases, model evaluation/observability, feature stores, MLOps/CI-CD for ML)
• Ability to partner deeply with engineering and data science, write clear PRDs, reason about APIs and data contracts, and interrogate dashboards/queries
• Excellent written and verbal communication, with the ability to simplify complexity and influence at all levels
• Bias to action with structured prioritization; skilled at balancing speed and quality in a regulated environment

Desirable

• As a Senior Product Manager focused on AI, you will own the strategy and roadmap for AI-powered capabilities across the SafeRide platform. You will work closely with engineering, data science, design, operations, and compliance teams to deliver features that improve reliability, efficiency, and equity of access
• This is a hands-on leadership role for a product manager who can turn ambiguous opportunities into measurable, impactful outcomes
• Define and drive the AI product strategy and multi-quarter roadmap across use cases such as member communications (chat, IVR), dispatch optimization, driver/provider quality, fraud/waste/abuse detection, and operations tooling
• Lead problem discovery and solution validation through user research with members, clients, and internal operators; translate insights into hypotheses, PRDs, and success metrics
• Partner with ML engineers and data scientists to ship model-backed experiences (LLMs, RAG, classification, ranking). Develop evaluation plans, A/B tests, and human-in-the-loop workflows
• Establish product guardrails for safety, bias, privacy, and compliance (including HIPAA). Align with InfoSec and Legal on responsible AI practices and governance
• Drive build/buy/partner decisions for AI infrastructure (e.g., model providers, vector databases, observability, feature stores) balancing velocity, cost, and risk
• Instrument products end-to-end with telemetry, offline/online metrics, and post-launch monitoring; iterate quickly based on data and feedback
• Communicate progress, tradeoffs, and results to executives and cross-functional stakeholders through clear artifacts and decision memos
• Mentor PMs and influence peers by modeling strong product thinking, urgency, and ownership