Grégory Mermoud

AI/ML Distinguished Engineer at Cisco | Computer Scientist | Innovator | Engineering Leader

I am a Distinguished Engineer (Senior Director) with a decade of hands-on experience in building highly scalable machine learning systems. I pioneered the use of AI/ML at Cisco, both from a technology and product perspective. I strive to foster scientific, engineering, and operational excellence, especially in the area of large-scale system design, machine learning, software engineering, data science, and data engineering.

I led multiple products and teams through their entire lifecycle, from ideation to market. I work best at the intersection of architecture, business, engineering, and technology, where I can influence and align vision, architecture, roadmap, research, and execution.

As an innovator, my mission is to deliver disruptive (non-incremental) technology, leading through the entire lifecycle, from ideation, team hiring, research and development, product design, system architecture, intellectual property, engineering execution, up to first customer shipment.


Innovation and Research

I authored 260 patents (180+ already issued) in the area of machine learning and artificial intelligence. I also published many peer-reviewed papers and book chapters. Furthermore, I published two monograms with Springer: my PhD thesis (volume 93) and the proceedings of DARS2010 (volume 83).

Product Development

I co-architected and lead the development of 5 key products at Cisco:

  • Predictive Networks (2022) is the core technology underpinning two products ( ThousandEyes WAN Insights , Cisco SD-WAN Analytics ) that brought for the first time learning capabilities to enterprise networks and the Internet. Routers proactively redirect application traffic away from paths that have a high probability of experiencing disruptions, thereby improving significantly the application experience of the end user. See Predictive Networks at Cisco for further details about the technology.
  • Cisco AI Network Analytics (2019) is a cloud-based machine learning platform that provides visibility and learns from 2500+ enterprise networks across the world. Upon its launch, the product was the first one to perform adaptive, ML-driven network assurance. See this blog post for further details.
  • Cisco AI Endpoint Analytics (2020) is built on the same platform as Cisco AI Network Analytics, the product protects organizations from ransomware with ML-based anti-spoofing technology. Using behavioral modeling and trust analytics, we detect devices that exhibit unusual patterns of communication typical of a rogue device attacking the network. See this blog post for further details.
  • Self Learning Networks (2016) is a cybersecurity product capable of zero-day attack detection by turning every router and switch into a security agent that ingests locally NetFlow telemetry, model the behavior of every device in the network and trigger alerts in case of anomalies. SLN uses a hyper-distributed learning architecture capable of processing up to 100 billion events a day across 5000 routers. Now part of Cisco Secure Network Analytics .
Team Leadership

I built 3 engineering teams built from the ground up. This amounts to more than 50 engineers hired, coached, and managed across a broad range of geographies and disciplines (software engineering, machine learning, data science, cloud engineering, data visualization, front-end design).

Software Development

I have a broad expertise in managing agile teams to deliver reliable software using continuous integration and delivery strategies, test automation, code reviews, pair programming, and cloud devops. I wrote a popular handbook that shows why I believe that building software that meets both customer requirements and deadlines is possible.

Customer Impact

I lead the design and development of five products at Cisco used by thousands of enterprise customers, including many Fortune 50/500, from inception to customer delivery in production. I attended dozens of key customer meetings over the years, both to pitch our vision and technological capabilities, and to understand their pain points and adapt our solutions accordingly.

Technological Breadth

My 10-year tenure at Cisco allowed me to gain insights into a broad technological landscape, including cybersecurity, smart grids, Internet of Things, wireless networks, internet architecture, endpoint classification, and cloud computing. Prior to that, I had worked closely with micro-engineers on the design of Micro-Electro-Mechanical Systems (MEMS) and with chemists on the self-assembly of large molecules. I also have a general interest in the area of climate science, energy, and sustainability.

Key Skills

Every now and then, I read again Matt Might's 'What every computer science major should know' and I realize that I will never quite live up to its standards. Yet, I found it to be an excellent North Star for my lifelong learning plan. The list below is a compilation of topics that I have seriously used and/or studied at some point in my career, but some might not be so fresh, especially the theoretical part (they are marked with an asterisk).

  • Software & Languages: Python, SQL, JAX, PyTorch, Polars, Apache Spark, C++*, Kafka*, Scala*, Containers, Git, GitHub, GitLab, CI/CD, Web development*
  • Data & ML: Machine Learning (supervised, unsupervised, semi-supervised, self-supervised, transfer learning, reinforcement learning, tree methods, linear methods), Deep Neural Networks, Transformers, Large Language Models, Differential Programming, Statistical Modeling, Data Engineering, Distributed Computing, Database Design, Data Warehousing, Data Visualization
  • Computer Science: System Architecture, Performance Evaluation, Computer Architecture, Networking, Cloud Architecture, Linux, Mac OS X, Compilers*, Multi-threading*
  • Leadership: Agile Methodologies, Innovation, Intellectual Property, Technical Writing, Public Speaking, User Experience, Requirement Analysis


Distinguished Engineer (Senior Director)


Delivering best-in-class machine reasoning and code generation for detection and remediation of IT infrastructure issues powered by Large Language Models (LLMs), reinforcement learning and differential programming (JAX).

As a Distinguished Engineer, I strive to live up to these standards.

October 2022 - present

Principal Engineer (Director)

Cisco, Predictive Networks

Architect and global engineering lead of the internal startup of 25 engineers that developed Predictive Networks. This award-winning technology now underpins two products ( ThousandEyes WAN Insights , Cisco SD-WAN Analytics) and leverages Cisco's unparalleled visibility into the Internet to endow enterprise networks with predictive capabilities. We worked with amazing customers across the world, including many Fortune 50/500.

January 2019 - September 2022

Head of Product Development (Senior Leader)

Cisco, Enterprise Networking Group

I was the Senior Technical Leader in charge of the machine learning group, as well as the development of the Cisco AI platform , a cloud-based machine learning platform that provides visibility and learns from enterprise networks across the world. I led the R&D process from white-boarding to the completion of first customer shipment. Today, it is routinely used by 3000+ customers across the world.

My role was a highly technical mix of research, system architecture, algorithm design, software development, product, and engineering management. I have hired and managed a multidisciplinary team of 12 software engineers, machine learning specialists and data scientists.

July 2016 - December 2018

Head of Machine Learning (Leader)

Cisco, Security Business Group

I was the Technical Leader in charge of the machine learning and software development group for Stealthwatch Learning Network (SLN), Cisco's next-generation cyber security platform, now known as Cisco Secure Network Analytics . This product turns every router and switch into a security agent capable of analyzing the behavior of every host and device on the network and triggering alerts in case of anomalies.

May 2014 – June 2016

Senior Software Engineer

Cisco, Internet of Things Group

Design and development of many machine learning algorithms, visualizations, and data analytics pipelines for Low-power Lossy Networks (LLNs).

  • Technical project lead, architect and main developer of the first service assurance prototype for the Internet of Things.
  • Lead developer of the very first prototype of Edge Computing at Cisco.
  • Hired and managed a small team of 3 software engineers.
Jun 2012 – May 2014


École Polytechnique Fédérale de Lausanne ( EPFL )

PhD in Computer Science
PhD thesis on intelligent distributed systems, with a strong emphasis on robotics and self-assembling systems; other achievements included:
  • Selected among the top 3 PhD robotics theses in Europe in 2012;
  • Author of 10 peer-reviewed papers in international conferences and journals, 1 patent, and 1 book chapter;
  • Supervision of more than 10 future engineers in various fields (computer science, systems of communication, mechanics, micro-engineering, physics);
  • Co-editor of the 10th International Symposium on Distributed Autonomous Robotics Systems (DARS2010).

École Polytechnique Fédérale de Lausanne ( EPFL )

Master in Computer Science

Focus on compiler construction and optimization, formal methods, machine learning and distributed intelligence.



A short article to help you understand what's happing under the hood of ChatGPT and co. 🤖

The surprising way philosophy of science can help you become a better data scientist.

My teams have always thought that building software that meets both customer requirements (by solving a key problem) and deadlines is possible. We have been historically pretty successful in doing so, and we strive to improve even further. This handbook introduces the foundational ideas and principles that have helped us in the past, and that we are committed to live by in the future.

There is a subtle, yet fundamental reason why automation is a key ingredient in the success of AI in networking and many other areas: causality. Note: the article is divided into two parts (direct link to the second).

I wrote this guide based on my experience managing various remote teams at Cisco. It provides practical pieces of advice for engineering teams on how to communicate effectively, in particular on technical matters.