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About This Role
The application window is expected to close on: 10/17/2026Meet the Team
Cisco’s AI Platform organization is building MyAgent—Cisco's enterprise AI platform powering intelligent, autonomous, and contextual assistance across the enterprise. We enable the engineering organization to improve and optimize engineering processes and infrastructure, delivering an exceptional developer and user experience for building next-generation products. In this role, you will direct the activities of a software systems development function, software applications development function, and a software quality assurance function for software enhancements and new products, including cloud-based and internet-related tools.
We are seeking an exceptional, hands-on, high-velocity Director of Engineering for AI Mobile, Desktop& Surfaces Development to architect, build, and lead the client surface ecosystem for MyAgent. You will lead a compact, elite engineering pod of 5–8 senior builders while remaining deeply hands-on in code delivery, system architecture, and runtime performance. You will own the primary interaction surfaces across mobile (iOS/Android), desktop (macOS/Windows), and embedded environments, delivering an extensible, modular architecture that allows engineers across Cisco to build, extend, and control their own domain experiences with native multimodal AI capabilities.
Your Impact
As the Director of Engineering, you will lead from the front, setting the technical direction and performing the end-to-end delivery of client applications and multimodal surfaces across all platforms.
- Write production code, conduct rigorous architecture and PR reviews, and set uncompromising quality benchmarks across all client codebases.
- Build, mentor, and direct an elite, high-slope engineering pod (5–8 senior engineers) focused on rapid iteration, low latency, and robust client software engineering.
- Own technical roadmaps, sprint planning, architectural achievements, and continuous release cadences across mobile, desktop, and embedded client surfaces.
- Lead technical feasibility spikes and rapid prototypes for emerging AI interaction paradigms, local inference runtimes, and native device hardware hooks.
- Architect, build, and ship high-performance native client applications across iOS, Android, macOS, and Windows with sub-second startup times and fluid animations.
- Build low-latency streaming conversational interfaces supporting real-time token streaming, rich markdown rendering, dynamic tables, and interactive UI widgets.
- Integrate native multimodal AI capabilities directly into client runtimes, including real-time bidirectional streaming audio/voice, computer vision, screenshot/screen-context capture, and accessibility hooks.
- Optimize client-side media pipelines (audio capture, frame buffer sampling, video streaming) for minimal battery consumption, memory overhead, and network bandwidth.
- Architect a modular presentation framework that empowers distributed engineering teams across Cisco to build, test, and control their own domain-specific UI cards and workflows without modifying core client binaries.
- Define and implement robust client-side event buses, state synchronization pipelines, local-first disk caching, background execution, and offline resilience.
- Establish unified component mapping between native client surfaces, web runtimes, and backend agent orchestration layers.
- Profile memory footprints, eliminate UI thread blocking, optimize cold-start times, and guarantee smooth 60/120 FPS rendering across varied hardware profiles.
- Enforce zero-trust enterprise security standards, automated PKCE authentication, dynamic workload identity bootstrapping (RFC 8693 token exchange), and secure on-device credential storage (Keychain, Keystore, DPAPI).
- Instrument rich client telemetry, crash analytics, real-time performance monitoring, and session tracing integrated into enterprise observability platforms.
Minimum Qualifications
- Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, Electrical Engineering, or a related technical field or equivalent practical work experience.
- 10+ years of professional software engineering experience building and shipping software products.
- 4+ years of experience leading, managing, or mentoring software engineering teams.
- Experience designing and developing client applications across mobile (iOS or Android) or desktop (macOS or Windows) platforms.
- Experience programming in one or more client-side languages (e.g., Swift, Kotlin, C++, Rust, TypeScript, or equivalent)
Preferred Qualifications
- Master’s degree or PhD in Computer Science, Human-Computer Interaction (HCI), or a related technical field.
- Experience managing compact, high-velocity engineering pods (5–8 senior builders) while actively writing production code and reviewing PRs.
- Demonstrated experience building and shipping native client applications from the ground up across iOS, Android, macOS, and Windows using modern frameworks (SwiftUI, Jetpack Compose, C++, Rust).
- Experience architecting client interfaces for generative AI, streaming chat completions, token-by-token streaming, markdown rendering, tool execution feedback, or multimodal interactions.
- Experience integrating bidirectional streaming audio/voice, computer vision, screen-context capture, or client-side media pipelines (WebRTC, CoreAudio, MediaCodec, Opus).
- Experience designing decoupled, pluggable UI component architectures supporting multi-tenant or multi-team contribution models.
- Experience with real-time streaming protocols (WebSockets, gRPC, Server-Sent Events / SSE), complex state machines, local-first disk caching, and offline synchronization.
- Experience using on-device hardware acceleration for local model inference (Apple Neural Engine, CoreML, ONNX Runtime, DirectML).
- Experience profiling memory, optimizing cold-start times, achieving 60/120 FPS rendering, and implementing zero-trust authentication (PKCE, OAuth token exchange, Keychain/Keystore/DPAPI).
- Contributions to open-source client frameworks, developer tools, or UI component libraries.
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 $250,600.00 to $362,600.00 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:
$250,600.00 - $416,900.00
Non-Metro New York state& Washington state:
$241,400.00 - $371,000.00
- 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 $241K-$416K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($329K) sits 55% above the category median. Disclosed range: $241K to $416K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Cisco AI Hiring
Cisco has 24 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer. Positions span San Jose, CA, US, Milpitas, CA, US, San Francisco, CA, US. Compensation range: $172K - $431K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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