Principal Product Manager – AI Platform

interface.ai•San Francisco, CA

3d•Onsite

About The Position

Interface.ai is redefining how financial institutions serve their members through agentic AI and conversational AI. The company has raised $30M in Series A funding and is cash-flow positive. The mission is to democratize financial wellness for every American. The opportunity is to own the AI platform that every product at the company depends on, which is considered the competitive moat. The role involves defining the future of the platform, including what to consolidate, invest in, or sunset. As a senior individual contributor in a team of 3-4 Product Managers, the role will set technical direction, mentor, and raise the quality bar, while partnering with engineering leadership to scale the system to millions of end users at startup speed.

Requirements

8+ years in product management, with 3+ years at Staff/Principal level on platform, AI/ML, or infrastructure products.
Owned a multi-component platform serving multiple product teams, not just a single product.
Deep technical fluency: can read code, review architecture, and debug production issues alongside engineers.
Experience with AI/ML platforms: LLM orchestration, model evaluation, fine-tuning, RAG, agentic frameworks, or similar.
Experience governing platform contracts and managing how internal teams consume capabilities.
Built in enterprise SaaS environments with multi-tenant, compliance, and governance requirements.
AI-native workflow: You build with AI daily — creating agents, automating analyses, prototyping with code, building custom workflows.

Nice To Haves

Financial services or regulated industry experience.
Voice/ASR/TTS systems and real-time latency-sensitive products.
LLM evaluation frameworks, multi-agent orchestration, or RLHF/RLAIF.
Knowledge graph or RAG-based retrieval systems.
Built a platform team from scratch.

Responsibilities

Own the AI platform, including agent runtime & orchestration (multi-agent orchestration, memory/context management, tool routing, goal planning, and policy-aligned behavior).
Define platform contracts: APIs, versioning, deprecation, and migration strategy.
Manage the model lifecycle: selection, evaluation, benchmarking, fine-tuning enablement, and prompt optimization.
Ensure inference performance & reliability: latency budgets, caching, batching, quantization, multi-region deployments, fallbacks, and safe rollout patterns.
Oversee safety & compliance: PII detection/masking, prompt/response safety policies, tenant isolation, SOC2/GDPR readiness, audit logs, and encryption.
Manage evaluation & quality: offline golden sets, regression testing, hallucination measurement, task success metrics, and production-to-improvement feedback loops.
Integrate voice & multimodal capabilities: ASR/TTS integration patterns, WER, interruption handling, turn-taking, and latency budgets for real-time conversational intelligence.
Provide pillar leadership: set technical direction, mentor, raise the bar, and have input on hiring. Prioritize competing demands, define the platform investment thesis, and protect the platform from becoming a feature factory.