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About This Role
The application window is expected to close on: 08/05/2026Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received.
Meet the Team
Design Foundations helps Cisco Networking product teams ship coherent, credible, buildable, and AI\-ready user experiences at scale.
We work horizontally across the Networking portfolio, including Meraki, Catalyst, ThousandEyes, and emerging agentic operations experiences. We bring together shared UX foundations across design systems, design language, AI experience standards, content strategy, methodology, governance, tooling, and design quality.
Design Foundations is helping teams embrace new methods, standards, tools, and operating models with clarity, consistency, and measurable impact.
Your Impact
As a Design Program Manager for AI Transformation\& Foundations, you will drive the operating model that helps Cisco Networking UX adopt AI\-assisted design practices at scale.
You will turn emerging AI work into clear programs with goals, owners, roadmaps, governance, adoption plans, and measurable outcomes. You will partner across Design Foundations, Design Ops, product design, research, engineering, security, AI, and product teams to ensure AI initiatives move from exploration to durable practice.
This role is for someone who can bring structure to ambiguity, build trust across teams, and help a distributed design organization change how it works. You will not just coordinate AI work; you will shape how it is prioritized, delivered, measured, communicated, and scaled!
WhatYou’llDo
- Own program management for AI Transformation and Design Foundations initiatives, including AI methodology, AgenticOps, AI education, governance, adoption, and cross\-portfolio design readiness.
- Build roadmaps, milestones, risks, dependencies, decision logs, reporting mechanisms, and operating rhythms for AI\-focused Foundations work.
- Coordinate across product design, design systems, research, content strategy, engineering, security, AI, and Design Ops to keep sophisticated AI workstreams aligned and moving.
- Establish lightweight governance models for AI design practices, including intake, prioritization, review cadences, adoption tracking, and leadership visibility.
- Partner with Design Technologists and Design Engineers to operationalize new practices, tools, agent skills, prototypes, and standards across the organization.
- Track adoption, risks, dependencies, and impact across AI design initiatives, surfacing decisions and tradeoffs for leadership.
- Build program playbooks, communication plans, and operating mechanisms that help teams understand what is happening and how to engage.
- Identify where teams are blocked by process, ownership, tooling, decision\-making, or cross\-functional alignment, and help create a path forward.
Minimum Qualifications
- Consistent track record in design program management, design operations, product operations, UX program management, product program management, or equivalent experience in a product\-focused organization.
- Experience driving sophisticated, cross\-functional programs across design, product, engineering, research, operations, or platform teams.
- Experience leading transformation, adoption, capability\-building, or change management programs across multiple teams.
- Experience creating roadmaps, operating models, program documentation, reporting mechanisms, and decision\-making processes.
- Strong understanding of product design, user\-centered design methods, design systems, research, or product development workflows.
- Strong communication, facilitation, partner management, and executive reporting skills.
- Ability to bring structure to ambiguous problems, navigate opposing priorities, and drive work forward without heavy oversight.
- Comfort working with AI\-assisted tools and curiosity about how AI changes design practice, product development, and team operations.
Preferred Qualifications
- Experience leading AI, transformation, design systems, design quality, design operations, or capability\-building programs.
- Experience working with design teams adopting AI tools, agentic workflows, automation, or new design technology.
- Experience with adoption metrics, maturity models, governance frameworks, enablement plans, or organizational change programs.
- Familiarity with Figma, Jira, GitHub, design systems, design tokens, research operations, or design\-to\-engineering workflows.
- Experience partnering with security, legal, procurement, platform, or AI teams to support responsible adoption of new tools or vendors.
Why Cisco?
At Cisco, we’re revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond. We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint.
Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you’ll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere.
We are Cisco, and our power starts with you.
Message to applicants applying to work in the U.S. and/or Canada:
The starting salary range posted for this position is $139,200\.00 to $201,400\.00 and reflects the projected salary range for new hires in this position in U.S. and/or Canada locations, not including incentive compensation\*, equity, or benefits.
Individual pay is determined by the candidate's hiring location, market conditions, job\-related skillset, experience, qualifications, education, certifications, and/or training. The full salary range for certain locations is listed below. For locations not listed below, the recruiter can share more details about compensation for the role in your location during the hiring process.
U.S. employees are offered benefits, subject to Cisco’s plan eligibility rules, which include medical, dental and vision insurance, a 401(k) plan with a Cisco matching contribution, paid parental leave, short and long\-term disability coverage, and basic life insurance. Please see the Cisco careers site to discover more benefits and perks. Employees may be eligible to receive grants of Cisco restricted stock units, which vest following continued employment with Cisco for defined periods of time.
U.S. employees are eligible for paid time away as described below, subject to Cisco’s policies:
- 10 paid holidays per full calendar year, plus 1 floating holiday for non\-exempt employees
- 1 paid day off for employee’s birthday, paid year\-end holiday shutdown, and 4 paid days off for personal wellness determined by Cisco
- Non\-exempt employees\*\* receive 16 days of paid vacation time per full calendar year, accrued at rate of 4\.92 hours per pay period for full\-time employees
- Exempt employees participate in Cisco’s flexible vacation time off program, which has no defined limit on how much vacation time eligible employees may use (subject to availability and some business limitations)
- 80 hours of sick time off provided on hire date and each January 1st thereafter, and up to 80 hours of unused sick time carried forward from one calendar year to the next
- Additional paid time away may be requested to deal with critical or emergency issues for family members
- Optional 10 paid days per full calendar year to volunteer
For non\-sales roles, employees are also eligible to earn annual bonuses subject to Cisco’s policies.
Employees on sales plans earn performance\-based incentive pay on top of their base salary, which is split between quota and non\-quota components, subject to the applicable Cisco plan. For quota\-based incentive pay, Cisco typically pays as follows:
- .75% of incentive target for each 1% of revenue attainment up to 50% of quota;
- 1\.5% of incentive target for each 1% of attainment between 50% and 75%;
- 1% of incentive target for each 1% of attainment between 75% and 100%; and
- Once performance exceeds 100% attainment, incentive rates are at or above 1% for each 1% of attainment with no cap on incentive compensation.
For non\-quota\-based sales performance elements such as strategic sales objectives, Cisco may pay 0% up to 125% of target. Cisco sales plans do not have a minimum threshold of performance for sales incentive compensation to be paid.
The applicable full salary ranges for this position, by specific state, are listed below:
New York City Metro Area:
$171,600\.00 \- $281,800\.00
Non\-Metro New York state\& Washington state:
$151,300\.00 \- $250,900\.00
- For quota\-based sales roles on Cisco’s sales plan, the ranges provided in this posting include base pay and sales target incentive compensation combined.
\*\* Employees in Illinois, whether exempt or non\-exempt, will participate in a unique time off program to meet local requirements.
Salary Context
This $139K-$281K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Cisco, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. Disclosed range: $139K to $281K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Cisco AI Hiring
Cisco has 16 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Engineer, AI Software Engineer. Positions span Seattle, WA, US, Milpitas, CA, US, San Jose, CA, US. Compensation range: $203K - $498K.
Location Context
AI roles in Denver pay a median of $199,950 across 66 tracked positions. That's 7% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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