Interface AI
Senior/Lead Product Manager - Core AI Platform
Interface AI, San Jose, California, United States, 95199
Senior/Lead Product Manager - Core AI Platform
Banking is being reimagined—and customers expect every interaction to be
easy, personal, and instant .
We are building a
universal banking assistant
that millions of U.S. consumers can use to transact across all financial institutions and, over time,
autonomously drive their financial goals . Powered by our proprietary
BankGPT platform , this assistant is positioned to
displace age-old legacy systems
within financial institutions and
own the end-to-end CX stack ,
unlocking a $200B opportunity
and potentially
replacing multiple publicly traded companies .
Ultimately,
our mission is to drive financial well-being
for millions of consumers.
With over two-thirds of Americans living paycheck to paycheck, 50% holding less than $500 in savings, and only 17% financially literate,
we aim to
put financial well-being on autopilot
to help solve this problem.
About the Role As a
Senior/Lead Product Manager
– Core AI Platform, you will own the vision, roadmap, and execution for the
Core Agentic AI Platform
that powers all interface.ai products.
This is a foundational, deeply technical role. You will define the platform primitives that enable:
Core agentic behavior (planning, goal routing, memory, context switching, tool use)
Safe and compliant AI operation in regulated environments (PII controls, auditability, policy enforcement)
Scalable, low‑latency inference and multi‑model orchestration across voice and chat experiences
Expansion beyond a single vertical by building reusable, configurable platform capabilities
You will partner tightly with Core AI Engineering, Research, Product Engineering, Design, and GTM/Delivery teams to turn platform capabilities into measurable product outcomes.
Key Responsibilities Define the Core AI Platform Vision and Roadmap
Set platform strategy for the agent runtime layer: multi‑agent orchestration, memory/context, tool routing, and policy‑aligned behavior.
Prioritize platform investments that scale across product lines and enable future vertical expansion.
Define clear platform contracts so product teams can reliably build on the platform.
Own the Model Lifecycle and Model Evolution Product Surface
Drive the roadmap for model selection, evaluation, fine‑tuning enablement, and benchmarking.
Partner with engineering to define workflows and requirements for fine‑tuning pipelines, dataset strategy, and safe experimentation.
Establish decision frameworks for when to prompt‑tune vs fine‑tune vs switch models, balancing quality, latency, and cost.
Inference Performance, Reliability, and Cost
Define product requirements for high‑throughput, low‑latency inference and runtime efficiency (caching, batching, quantization strategy, token efficiency).Establish reliability patterns: multi‑region deployments, fallbacks, graceful degradation, and safe rollouts (flags/canaries/rollback).
Build cost/latency governance: budgets, monitoring, and optimization priorities across high‑scale deployments.
Safety, Guardrails, and Compliance by Design
Own platform‑level requirements for automated PII detection/masking, prompt/response safety policies, and data handling controls.
Drive secure‑by‑default platform capabilities: tenant isolation, encryption expectations, retention controls, audit logs, and access control requirements.
Ensure the platform can support compliance needs (e.g., SOC2/GDPR readiness) through measurable controls and operational rigor.
Evaluation Harnesses and Production Quality Loops
Establish the eval strategy and roadmap: offline golden sets, regression testing, online quality metrics, and automated safety checks.
Define how teams measure factual accuracy, hallucination risk, task success, latency, and cost efficiency—then make it actionable via tooling and dashboards.
Create feedback loops from production to improve prompts/models/policies continuously.
Voice / Speech‑to‑Speech and Multimodal Enablement
Drive platform requirements for real‑time conversational intelligence: ASR/TTS integration patterns, latency budgets, and quality metrics (WER, interruption handling, turn‑taking).
Prioritize multimodal platform primitives that improve naturalness, responsiveness, and user trust in voice experiences.
Cross‑Team Alignment and Adoption
Partner with PMs and engineering leads across product lines to drive platform adoption, migration plans, and deprecation/versioning strategy.
Translate deep technical constraints into clear product trade‑offs and execution plans.
Maintain crisp documentation, onboarding paths, and operating rhythms for platform consumers.
Required Qualification
5+ years product management experience, ideally on platform, AI/ML, infra, or developer‑facing products.
Strong technical fluency: able to write product specs for model lifecycle, inference/runtime, evals, and safety systems; comfortable partnering daily with senior/staff engineers.
Experience defining platform interfaces and driving adoption across multiple product teams (APIs, versioning, migration strategy).
Proven ability to lead cross‑functional execution with measurable outcomes (metrics, dashboards, experiments).
Experience building in enterprise SaaS environments with multi‑tenant requirements, governance, and operational rigor.
Preferred Qualification
Experience with LLM systems, multi‑agent orchestration, and evaluation frameworks.
Familiarity with fine‑tuning, RLHF/RLAIF concepts, and prompt optimization loops (as product domains).
Experience with voice/ASR/TTS systems and real‑time latency‑sensitive product constraints.
Exposure to regulated domains (fintech, healthcare, insurance) and compliance‑driven product requirements.
Competitive salary, bonus, and equity. (Compensation may vary based on skills and location.). Base Salary Range 200-240k
Benefits
100% paid health, dental & vision care
401(k) match & financial wellness perks
Discretionary PTO + paid parental leave
Remote‑first flexibility
Mental health, wellness & family benefits
A mission‑driven team shaping the future of banking
At interface.ai, we are committed to providing an inclusive and welcoming environment for all employees and applicants. We celebrate diversity and believe it is critical to our success as a company. We do not discriminate on the basis of race, color, religion, national origin, age, sex, gender identity, gender expression, sexual orientation, marital status, veteran status, disability status, or any other legally protected status. All employment decisions at Interface.ai are based on business needs, job requirements, and individual qualifications. We strive to create a culture that values and respects each person's unique perspective and contributions. We encourage all qualified individuals to apply for employment opportunities with Interface.ai and are committed to ensuring that our hiring process is inclusive and accessible.
#J-18808-Ljbffr
easy, personal, and instant .
We are building a
universal banking assistant
that millions of U.S. consumers can use to transact across all financial institutions and, over time,
autonomously drive their financial goals . Powered by our proprietary
BankGPT platform , this assistant is positioned to
displace age-old legacy systems
within financial institutions and
own the end-to-end CX stack ,
unlocking a $200B opportunity
and potentially
replacing multiple publicly traded companies .
Ultimately,
our mission is to drive financial well-being
for millions of consumers.
With over two-thirds of Americans living paycheck to paycheck, 50% holding less than $500 in savings, and only 17% financially literate,
we aim to
put financial well-being on autopilot
to help solve this problem.
About the Role As a
Senior/Lead Product Manager
– Core AI Platform, you will own the vision, roadmap, and execution for the
Core Agentic AI Platform
that powers all interface.ai products.
This is a foundational, deeply technical role. You will define the platform primitives that enable:
Core agentic behavior (planning, goal routing, memory, context switching, tool use)
Safe and compliant AI operation in regulated environments (PII controls, auditability, policy enforcement)
Scalable, low‑latency inference and multi‑model orchestration across voice and chat experiences
Expansion beyond a single vertical by building reusable, configurable platform capabilities
You will partner tightly with Core AI Engineering, Research, Product Engineering, Design, and GTM/Delivery teams to turn platform capabilities into measurable product outcomes.
Key Responsibilities Define the Core AI Platform Vision and Roadmap
Set platform strategy for the agent runtime layer: multi‑agent orchestration, memory/context, tool routing, and policy‑aligned behavior.
Prioritize platform investments that scale across product lines and enable future vertical expansion.
Define clear platform contracts so product teams can reliably build on the platform.
Own the Model Lifecycle and Model Evolution Product Surface
Drive the roadmap for model selection, evaluation, fine‑tuning enablement, and benchmarking.
Partner with engineering to define workflows and requirements for fine‑tuning pipelines, dataset strategy, and safe experimentation.
Establish decision frameworks for when to prompt‑tune vs fine‑tune vs switch models, balancing quality, latency, and cost.
Inference Performance, Reliability, and Cost
Define product requirements for high‑throughput, low‑latency inference and runtime efficiency (caching, batching, quantization strategy, token efficiency).Establish reliability patterns: multi‑region deployments, fallbacks, graceful degradation, and safe rollouts (flags/canaries/rollback).
Build cost/latency governance: budgets, monitoring, and optimization priorities across high‑scale deployments.
Safety, Guardrails, and Compliance by Design
Own platform‑level requirements for automated PII detection/masking, prompt/response safety policies, and data handling controls.
Drive secure‑by‑default platform capabilities: tenant isolation, encryption expectations, retention controls, audit logs, and access control requirements.
Ensure the platform can support compliance needs (e.g., SOC2/GDPR readiness) through measurable controls and operational rigor.
Evaluation Harnesses and Production Quality Loops
Establish the eval strategy and roadmap: offline golden sets, regression testing, online quality metrics, and automated safety checks.
Define how teams measure factual accuracy, hallucination risk, task success, latency, and cost efficiency—then make it actionable via tooling and dashboards.
Create feedback loops from production to improve prompts/models/policies continuously.
Voice / Speech‑to‑Speech and Multimodal Enablement
Drive platform requirements for real‑time conversational intelligence: ASR/TTS integration patterns, latency budgets, and quality metrics (WER, interruption handling, turn‑taking).
Prioritize multimodal platform primitives that improve naturalness, responsiveness, and user trust in voice experiences.
Cross‑Team Alignment and Adoption
Partner with PMs and engineering leads across product lines to drive platform adoption, migration plans, and deprecation/versioning strategy.
Translate deep technical constraints into clear product trade‑offs and execution plans.
Maintain crisp documentation, onboarding paths, and operating rhythms for platform consumers.
Required Qualification
5+ years product management experience, ideally on platform, AI/ML, infra, or developer‑facing products.
Strong technical fluency: able to write product specs for model lifecycle, inference/runtime, evals, and safety systems; comfortable partnering daily with senior/staff engineers.
Experience defining platform interfaces and driving adoption across multiple product teams (APIs, versioning, migration strategy).
Proven ability to lead cross‑functional execution with measurable outcomes (metrics, dashboards, experiments).
Experience building in enterprise SaaS environments with multi‑tenant requirements, governance, and operational rigor.
Preferred Qualification
Experience with LLM systems, multi‑agent orchestration, and evaluation frameworks.
Familiarity with fine‑tuning, RLHF/RLAIF concepts, and prompt optimization loops (as product domains).
Experience with voice/ASR/TTS systems and real‑time latency‑sensitive product constraints.
Exposure to regulated domains (fintech, healthcare, insurance) and compliance‑driven product requirements.
Competitive salary, bonus, and equity. (Compensation may vary based on skills and location.). Base Salary Range 200-240k
Benefits
100% paid health, dental & vision care
401(k) match & financial wellness perks
Discretionary PTO + paid parental leave
Remote‑first flexibility
Mental health, wellness & family benefits
A mission‑driven team shaping the future of banking
At interface.ai, we are committed to providing an inclusive and welcoming environment for all employees and applicants. We celebrate diversity and believe it is critical to our success as a company. We do not discriminate on the basis of race, color, religion, national origin, age, sex, gender identity, gender expression, sexual orientation, marital status, veteran status, disability status, or any other legally protected status. All employment decisions at Interface.ai are based on business needs, job requirements, and individual qualifications. We strive to create a culture that values and respects each person's unique perspective and contributions. We encourage all qualified individuals to apply for employment opportunities with Interface.ai and are committed to ensuring that our hiring process is inclusive and accessible.
#J-18808-Ljbffr