Senior Staff Product Manager, Splunk AI Foundations

Cisco

Confirmed live yesterday High trust
Remote

Quick summary

Work type
Remote
Location
Seattle, WA
Salary
$179,000–$254,300 / yr
Posted
28 days ago
Freshness
Confirmed live yesterday
Closes
Sep 25, 2026

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $217k
This role $217k
$170k most similar roles pay here $264k

This role pays more than 55% of similar roles. Most pay $188,437–$246,075 — the shaded band above. At the midpoint, this role pays about $217k versus about $217k for comparable roles.

Based on 240 similar postings.

Employer

About Cisco

Cisco Systems is the world''s leading networking technology company, designing and manufacturing networking hardware, telecommunications equipment, and cybersecurity solutions for businesses and governments. Industry: Networking Technology & Cybersecurity

Cisco currently has 196 open roles on FindRole.

Listed pay typically runs $167,700–$245,200 across 196 roles with salary data.

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At a glance

TL;DR · Senior Staff Product Manager, Splunk AI Foundations

Senior Staff Product Manager - Splunk AI Foundations joins the AI Foundations team to architect the "AI Engine Room" powering Cisco and Splunk products. This role involves managing the model substrate, including pretraining, post-training, evaluation, and inference stacks for machine data metrics like logs, traces, and events. You will define roadmaps for high-scale AI, develop domain-specific foundation models such as the Cisco Time Series Model, and build agentic workflow frameworks using tool-calling and reasoning models to enable autonomous operations. Key responsibilities include overseeing model quality through golden datasets, establishing inference unit economics, and ensuring Responsible AI governance. The role requires expertise in the AI/ML lifecycle, including SFT, RLHF, and MLOps. Technical requirements include proficiency in SQL and Python for data analysis and managing large-scale data flows into AI-ready formats within security and observability contexts.

What does a Product Manager earn in Washington?

Median $207000 from 56 postings across 17 companies.

See salary data

What you'll do

  • Define the roadmap for the model substrate including model families, sizes, context lengths, and tokenization for machine data.
  • Manage the productization of domain-specific foundation models from research checkpoints to versioned, documented, and supported products.
  • Establish rigorous evaluation frameworks using golden datasets and benchmarks to measure accuracy, hallucination rates, and performance.
  • Develop agentic workflow frameworks including planning loops, tool schemas, and guardrails for autonomous AI agents.
  • Standardize AI skills across products by implementing common agentic protocols and universal connectors.
  • Ensure Responsible AI compliance by managing red-teaming, safety evaluations, data provenance, and inference unit economics.
  • Partner with marketing and sales to package capabilities into customer-facing offerings with clear pricing and documentation.

What we're looking for

  • Bachelor's degree plus 12 years of experience in Product Management for AI/ML platforms; or Master's plus 8 years; or PhD plus 5 years.
  • Experience in the AI/ML lifecycle, including model fine-tuning (SFT/RLHF), orchestration architectures, and data ingestion pipelines.
  • Experience implementing security protocols, compliance frameworks, and guardrails within AI or software development platforms.
  • Experience building or managing observability tools, tracing, or monitoring systems for distributed systems or ML models.
  • Proficiency in SQL and Python for data analysis and managing large-scale data flows into AI-ready formats.
  • Advanced degree (Master's or Ph.D.) in a quantitative field or an MBA (preferred).
  • Hands-on experience with the end-to-end model development lifecycle, including training, evaluation, serving, and drift monitoring (preferred).
  • Knowledge of MLOps/LLMOps in networking or cybersecurity, including GPU capacity planning and deployment in regulated environments (preferred).

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