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About This Role
The application window is expected to close on: 04/20/2026Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received.
Meet the Team
The AI Platforms and Enablement team is the engine behind Cisco’s internal AI transformation. Our mission is to build and scale CIRCUIT, Cisco’s universal enterprise AI platform designed to collapse information silos and amplify productivity through intelligent, agentic experiences.
CIRCUIT is more than a platform—it is the foundation for how Cisco will reimagine work through secure, scalable AI. Our team combines platform development with enterprise enablement, ensuring that business units can adopt AI capabilities, integrate proprietary data, and transform legacy workflows into intelligent, autonomous experiences.
The organization operates through an Eight\-Pillar CIRCUIT model spanning Platform Engineering, Solutions Engineering, Process Engineering, Product Management, UX Design, Technical Program Management, Innovation\& Advanced Architecture, and Strategy\& Operations. Together, these pillars balance specialized execution with cross\-functional collaboration to move from invention to production at enterprise scale.
Within this model, Innovation\& Advanced Architecture serves as the Invention Lab. This team is responsible for solving Cisco’s most complex AI architecture challenges, incubating breakthrough capabilities, and establishing the path from invention to hardened, production\-ready platform features.
We are looking for a Distinguished Engineer to define the long\-term technical direction for CIRCUIT, advance the state of enterprise agentic systems, and help translate Cisco’s internal AI capabilities into scalable platform assets and future product opportunities.
Your Impact
As the Distinguished Engineer for Innovation\& Advanced Architecture, you will serve as the senior technical authority for Cisco’s most strategic and complex AI architecture challenges. You will define the technical north star for CIRCUIT’s evolution and lead the invention\-to\-productization path across Cisco’s enterprise AI ecosystem, guiding where the platform should go next and which technical bets will unlock long\-term value.
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You will define the long\-term architecture for Cisco’s enterprise AI platform, encompassing agentic systems, multi\-agent orchestration, LLM integration, search and AI convergence, retrieval and memory frameworks, evaluation and reliability systems, human‑in‑the‑loop controls, and scalable data pipelines. Your work will ensure the platform remains modular, secure, resilient, interoperable, and future‑ready.
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You will lead the design and resolution of the organization’s most complex technical challenges, including multi‑agent system reliability, orchestration frameworks, long‑term context retention, model routing, retrieval patterns, trust mechanisms, and enterprise‑scale AI performance. You will set architectural principles and reference patterns that reduce fragmentation and enable consistent execution across the eight‑pillar operating model.
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You will create and drive a clear path that converts advanced technical invention into production‑grade platform capabilities. Partnering closely with Platform Engineering, you will ensure new ideas are designed for production from the outset and hardened into operationally ready services. In collaboration with Product Management, you will align technical innovation with CIRCUIT’s long‑term roadmap and value proposition.
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You will act as the primary architectural bridge across Platform Engineering, Solutions Engineering, Process Engineering, Product, and other Cisco business units, driving alignment on standards, frameworks, and reference architectures. You will work across corporate functions to ensure internal AI efforts build on CIRCUIT in a way that is secure, interoperable, and extensible.
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You will lead rapid exploration of emerging areas such as agent collaboration, autonomous planning, proactive workflows, tool use, long‑term memory, and new interaction models, evaluating technical feasibility, scalability, and enterprise readiness. You will stay closely connected to advances in AI research, open‑source ecosystems, model providers, cloud platforms, and enterprise AI frameworks, guiding adoption where it creates durable advantage for Cisco.
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You will represent Cisco as a recognized technical authority in enterprise AI, engaging with research communities, partners, customers, and industry ecosystems. You will translate complex technical strategy into clear direction for executives, senior engineering leaders, and strategic stakeholders, and support high‑level customer and partner conversations by articulating Cisco’s AI architecture approach and lessons learned from operating CIRCUIT at scale.
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You will elevate the technical maturity of the organization through mentorship, architectural reviews, standards setting, and hands‑on guidance. You will coach senior engineers and technical leaders, build a strong bench of architectural talent, foster a culture of high‑velocity experimentation and rigorous engineering quality, and contribute to Cisco’s technical reputation through patents, white papers, architectural standards, and thought leadership.
Minimum Qualifications
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Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
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17\+ years of experience in software engineering, technical architecture, distributed systems, AI/ML, or platform engineering; advanced degree may substitute for some experience.
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Demonstrated experience as a principal engineer, architect, or senior technical leader in large\-scale enterprise environments.
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Proven ability to define technical strategy and drive architectural direction across multiple organizations and business domains.
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Strong track record of leading complex, cross\-functional technical initiatives from concept through production.
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Experience with AI/ML systems, distributed systems, cloud\-native infrastructure, or large\-scale data platforms.
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Demonstrated history of architectural influence through standards, patents, publications, technical papers, or comparable contributions.
Preferred Qualifications
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Deep expertise in generative AI, LLM integration, multi\-agent systems, RAG, evaluation frameworks, and enterprise AI architecture.
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Experience taking research concepts or early prototypes through productization into secure, scalable, production\-ready offerings.
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Strong familiarity with model orchestration, memory systems, tool invocation, agent reliability, and enterprise trust controls
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Experience in multi\-cloud and hybrid environments, including GCP, AWS, Azure, and on\-prem infrastructure.
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Strong network within AI research, open\-source, or partner ecosystems.
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Experience mentoring elite engineering teams in high\-velocity, experimental environment.
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Prior customer\-facing or partner\-facing experience providing technical strategy, architecture guidance, or executive\-level AI advisory support.
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Experience with enterprise search, data integration, developer platforms, or large\-scale internal productivity systems.
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 $273,200\.00 to $384,800\.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:
$273,200\.00 \- $442,600\.00
Non\-Metro New York state\& Washington state:
$267,600\.00 \- $390,300\.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 $273K-$442K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $188,800. This role's midpoint ($357K) sits 114% above the category median. Disclosed range: $273K to $442K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Cisco AI Hiring
Cisco has 14 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Software Engineer. Positions span Austin, TX, US, Research Triangle Park, NC, US, San Jose, CA, US. Compensation range: $222K - $490K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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