What is a Machine Learning Engineer at Cisco?

Cisco is evolving far beyond its roots as a hardware networking giant. Today, the company is aggressively integrating Artificial Intelligence into its massive ecosystem of security, collaboration (Webex), and observability products. As a Machine Learning Engineer at Cisco, you are not just building models; you are embedding intelligence into the infrastructure that powers the internet.

This role is critical because Cisco sits on a unique vantage point of global data. You will work on challenges ranging from optimizing network traffic routing using predictive algorithms to deploying Large Language Models (LLMs) for automated security threat detection and intelligent customer agents. You will likely join teams such as the Security Business Group, Collaboration, or Enterprise Networking, where the focus is on applying AI to solve real-world connectivity and security problems at scale.

The work environment balances the stability of an established tech leader with the agility required to adopt Generative AI. You will be expected to bridge the gap between theoretical data science and production-grade engineering, ensuring that models are not only accurate but also robust enough to run on edge devices or within massive cloud environments.

Getting Ready for Your Interviews

Preparation for Cisco requires a shift in mindset. You need to demonstrate that you can apply ML concepts to practical engineering problems. Do not just memorize definitions; be ready to explain how you implemented solutions.

Key Evaluation Criteria

Technical Proficiency & Code Navigation – 2–3 sentences Cisco places a high value on your ability to work with existing codebases. You won't just be writing algorithms from scratch; interviewers often test your ability to read, debug, and navigate complex repositories (such as agent frameworks) to understand logic flow and reward calculations.

Generative AI & LLM Fluency – 2–3 sentences Given the current strategic direction, there is a heavy emphasis on Large Language Models. You must be able to discuss modern architectures, RAG (Retrieval-Augmented Generation), and agentic workflows, and ideally have a "CV story" ready that highlights a specific LLM project you have built or optimized.

Domain Application & Problem Solving – 2–3 sentences You need to show how ML applies to Cisco’s domain—networking and security. Interviewers evaluate how you structure problems: can you take a vague requirement (e.g., "detect anomalies in traffic") and break it down into data requirements, model selection, and evaluation metrics?

Interview Process Overview

The interview process at Cisco is generally structured to be thorough yet respectful of your time. It typically begins with a recruiter outreach that is more than just a formality; recruiters often dig into your specific project experiences right away to gauge your alignment with current needs, particularly regarding Generative AI. If you pass this screen, you will move into technical rounds.

The technical stage is known for its practical nature. Rather than purely abstract whiteboard coding, you may face a "practical screen" or a take-home style assessment done live. Candidates have reported being given access to a code repository (e.g., an AI agent framework) and asked to locate specific logic, such as where rewards are calculated, or to debug a specific module. This tests your ability to function as a software engineer who understands ML systems, not just a data scientist.

Following the technical screen, the onsite (or virtual onsite) loop consists of multiple rounds covering deep technical skills, system design, and behavioral fit. Cisco values "Cisco culture," which emphasizes collaboration and conscious inclusion, so expect a dedicated portion of the interview to focus on how you work with cross-functional teams.

This timeline illustrates a standard progression from the initial recruiter screen through to the final offer stage. Use this to pace your preparation; ensure your "project stories" are polished for the early stages, while reserving deep architectural study for the later onsite rounds. Note that for specialized MLE roles, the technical screen can be quite rigorous and specific to the team's tech stack.

Deep Dive into Evaluation Areas

To succeed, you must demonstrate depth in specific technical areas. Cisco's interviews for MLE roles have pivoted significantly toward modern AI stacks.

Large Language Models (LLMs) & GenAI

This is currently the most critical evaluation area for many teams. Interviewers want to know if you understand the "new stack" of AI. It is not enough to know what a Transformer is; you need to understand how to build applications on top of them.

Be ready to go over:

• Agent Frameworks – Understanding how autonomous agents plan, execute, and evaluate tasks.
• RAG (Retrieval-Augmented Generation) – Architecting systems that retrieve context to reduce hallucinations.
• Prompt Engineering vs. Fine-Tuning – Knowing when to use which approach for specific business problems.
• Advanced concepts – Efficient fine-tuning (LoRA/QLoRA), vector databases, and context window optimization.

Example questions or scenarios:

• "Here is a repository for an agent framework. Can you walk me through the code and identify where the reward function is calculated?"
• "How would you design a chatbot that answers technical support questions using Cisco's internal documentation?"
• "Describe a challenge you faced when deploying an LLM in production. How did you handle latency?"

Data Science Fundamentals & Statistics

While GenAI is the focus, the foundations remain essential. You may be tested on your grasp of core data science principles to ensure you aren't just using APIs blindly.

Be ready to go over:

• Model Evaluation – Precision, Recall, F1-score, and ROC-AUC, specifically in the context of imbalanced datasets (common in security/fraud).
• Classical ML Algorithms – Random Forests, Gradient Boosting, and Clustering (K-Means).
• Data Processing – Feature engineering, handling missing data, and dimensionality reduction.

Example questions or scenarios:

• "How do you handle data drift in a deployed model?"
• "Explain the bias-variance tradeoff to a non-technical stakeholder."

Software Engineering & Code Fluency

Cisco hires Engineers. You must be proficient in Python and comfortable navigating production-level code.

Be ready to go over:

• Code Navigation – Quickly reading and understanding a new codebase.
• Python Proficiency – Data structures, list comprehensions, and efficient data manipulation (Pandas/NumPy).
• Version Control – Git workflows and collaboration best practices.

Example questions or scenarios:

• "Given this snippet of Python code, identify the bug and refactor it for better performance."
• "How would you structure a Python project for a machine learning pipeline to ensure reproducibility?"

Key Responsibilities

As a Machine Learning Engineer at Cisco, your day-to-day work will revolve around translating complex data into actionable intelligence. You will be responsible for the end-to-end lifecycle of ML models, from data ingestion to deployment and monitoring.

You will collaborate closely with product managers and backend engineers. For example, if you are in the Eta Routing or Networking teams, you might build models that predict network congestion and automatically reroute traffic. If you are in Collaboration, you might work on noise cancellation or real-time translation features.

A significant portion of your time will be spent on MLOps and Infrastructure. You aren't just training models in a notebook; you are containerizing them (Docker/Kubernetes) and ensuring they run efficiently on Cisco’s cloud or edge hardware. You will also likely be tasked with integrating Generative AI capabilities into existing products, requiring you to stay up-to-date with the rapidly changing landscape of open-source models and frameworks.

Role Requirements & Qualifications

Cisco looks for candidates who combine academic understanding with engineering rigor.

• Technical Skills

  • Must-have: Strong Python programming, proficiency with PyTorch or TensorFlow, and experience with SQL/NoSQL databases.
  • Must-have: Hands-on experience with LLM frameworks (LangChain, LlamaIndex, Hugging Face) and vector databases.
  • Nice-to-have: Experience with C++ (for edge optimization), Kubernetes, and cloud platforms (AWS/GCP/Azure).

• Experience Level

  • Typically requires a BS/MS in Computer Science, Electrical Engineering, or related fields.
  • For Mid-to-Senior roles, 3+ years of industry experience is standard, with a preference for candidates who have shipped models to production.

• Soft Skills

  • Communication: Ability to explain complex AI concepts to network engineers or product managers who may not have an ML background.
  • Adaptability: The ability to pivot quickly as new technologies (like new foundation models) emerge.

Common Interview Questions

The following questions are representative of what you might encounter. They are drawn from recent candidate data and reflect Cisco's current focus on practical engineering and LLMs. Do not memorize answers; use these to identify the types of problems you need to be ready to solve.

Technical & Coding

• "Given a repository containing an RL (Reinforcement Learning) agent, locate the specific function where the reward is computed."
• "Write a Python function to process a stream of log data and identify anomalies based on a moving average."
• "How would you optimize a Transformer model for inference on a device with limited memory?"
• "Explain the difference between encoder-only and decoder-only architectures."
• "Implement a custom loss function in PyTorch for a multi-label classification problem."

Behavioral & Situational

• "Tell me about a specific project where you used Large Language Models. What was the outcome?"
• "Describe a time you had to debug a complex issue in a codebase you didn't write."
• "How do you handle disagreements with a product manager regarding the feasibility of an ML feature?"
• "Tell me about a time you failed to meet a deadline. How did you communicate it?"

Frequently Asked Questions

Q: How much focus is there on LeetCode-style algorithms? While you should be comfortable with data structures and algorithms (Medium difficulty), Cisco's MLE interviews often lean more toward practical coding—such as data manipulation or navigating a repo—rather than pure competitive programming puzzles.

Q: Do I need prior networking experience? No, prior networking experience is usually a "nice-to-have" rather than a requirement. However, you must demonstrate the curiosity and aptitude to learn the domain quickly. Domain knowledge in security or routing is a differentiator.

Q: What is the work-life balance like for this role? Cisco is widely reputed to have a strong work-life balance compared to other top-tier tech companies. The culture emphasizes sustainability and employee well-being, though this can vary slightly by team and release cycles.

Q: How long does the process take? The process is generally efficient. You can expect a timeline of 3–5 weeks from the initial recruiter screen to a final decision, depending on scheduling alignment.

Q: Is this a remote role? Many teams operate on a hybrid model. Cisco has a flexible approach ("Cisco Hybrid Work"), but specific requirements often depend on whether the team requires access to on-premise labs or hardware.

Other General Tips

Prepare your "LLM Story" Candidates have been rejected for not having a prepared narrative about their experience with LLMs. Even if your professional experience is limited, build a side project using LangChain or an open-source model so you can discuss the challenges of prompt engineering and context management intelligently.

Review Core Repositories Since you may be asked to navigate code, spend time reading open-source ML repositories on GitHub. Practice understanding the file structure of a typical agent framework or RAG pipeline. Being able to quickly orient yourself in a new codebase is a specific skill they test.

Focus on Collaboration Cisco prides itself on a culture of "Conscious Culture." In your behavioral answers, highlight how you support teammates, share knowledge, and contribute to an inclusive environment. Avoid answers that make you sound like a "lone wolf."

Know the Business Unit If you know you are interviewing for the Security or Collaboration team, research their recent product announcements. Mentioning how AI could improve that specific product demonstrates high intent and business acumen.

Summary & Next Steps

Becoming a Machine Learning Engineer at Cisco puts you at the intersection of massive scale and cutting-edge intelligence. You have the opportunity to work on systems that form the backbone of the internet, applying the latest in Generative AI to solve critical problems in security and connectivity. The role offers a blend of technical challenge and stable work-life balance that is hard to find elsewhere.

To succeed, focus your preparation on practical engineering skills. Move beyond theory; practice navigating codebases, debugging pipelines, and articulating your experience with LLMs and agentic frameworks clearly. Ensure you have a strong "project story" that highlights your hands-on capability.

This salary data provides a baseline for expectations. Compensation at Cisco is competitive and often includes a mix of base salary, performance bonuses, and RSUs. Seniority and location (e.g., San Jose vs. Atlanta) will significantly influence the final offer package.

You have the skills to excel in this process. Approach the interview with curiosity, show them your ability to build and debug real systems, and demonstrate how you can contribute to Cisco's AI-driven future. Good luck!