Samantha Guerriero, Developer in London, United Kingdom
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Samantha Guerriero

Verified Expert  in Engineering

Machine Learning Developer

London, United Kingdom
Toptal Member Since
April 12, 2021

Sam is a bilingual senior machine learning engineer with a background in AI and robotics and a passion for driving innovation. She dedicated the past five years to exploring the best ways to build, optimize, and automate ML pipelines, with a specialization in Google Cloud Platform and TensorFlow. During this journey, she has also had the opportunity to experience many aspects of a business outside of technical, from line management and recruiting to marketing, thought leadership, and pre-sales.


IOD Cloud Technologies Research Ltd.
Artificial Intelligence (AI), Machine Learning, PyTorch, Python...
Yields NV
Agile Software Development, Kubeflow, Machine Learning Operations (MLOps)...
Amazon Web Services (AWS), Snowflake, Data Build Tool (dbt), Dask, Python...




Preferred Environment

Linux, MacOS, Google Cloud Platform (GCP), XGBoost, Scikit-learn, Keras, Python, Docker, Amazon Web Services (AWS), Kubernetes

The most amazing...

...project I've led was a computer vision model that identified products from in-app images; >95% accuracy, 60% package-free product, and lots of plastic saved!

Work Experience

Technical Writer | Consultant

2021 - PRESENT
IOD Cloud Technologies Research Ltd.
  • Wrote over 30 articles as a ghostwriter and thought-leadership pieces for various data science, data, and AI companies.
  • Increased the online engagement on the website for client companies by around three times.
  • Performed in-depth research into topics ranging from TF Serving to ML model governance and data fabric.
Technologies: Artificial Intelligence (AI), Machine Learning, PyTorch, Python, Google Cloud AI, Azure Machine Learning, Amazon SageMaker, Deep Learning, Keras, TensorFlow, Technical Writing, Writing & Editing, Blogging

Kubeflow Expert

2023 - 2023
Yields NV
  • Developed a system requirement design document outlining the business use case, architecture design, and functional requirements of lift&shifting the current infrastructure to Kubeflow.
  • Consulted on the implementation plan for the lift&shift of the infrastructure, with a PoC delivered by the team in two-weeks time.
  • Consulted on the pros and cons of different solutions to Kubeflow, focusing on Vertex AI and other cloud solutions.
Technologies: Agile Software Development, Kubeflow, Machine Learning Operations (MLOps), Data Science, Kubernetes, DevOps, Technical Design

Machine Learning Engineer

2021 - 2022
  • Ideated, designed, and delivered the automated retraining pipeline for the fraud engine model.
  • Ideated, designed, and set up an internal package for machine learning, MLOps, and data functionalities.
  • Introduced a new set of best practices for data, code, and model testing, leading to increased coverage by 50%.
  • Consulted periodically with the data science team on optimization for the current model pipeline.
Technologies: Amazon Web Services (AWS), Snowflake, Data Build Tool (dbt), Dask, Python, TeamCity, Jenkins, Octopus Deploy, Linux, MacOS, XGBoost, Scikit-learn, Keras, Docker, Machine Learning, Statistical Methods, Database Design, Recruiting, Data Science, Machine Learning Automation, System Design, Cloud, Networking, Kubernetes, SQL, English, Automation, Google Structured Data, Batch Prediction, APIs, Deep Learning, Machine Learning Operations (MLOps), Content Writing, Artificial Intelligence (AI), Pandas, Big Data, Algorithms, Deep Neural Networks, Mathematics

Senior Machine Learning Engineer

2018 - 2021
  • Led and delivered multiple consulting projects in data science, AI optimization and automation from the qualification call to client engagement and project delivery with and NPS of 100.
  • Conceptualized the AI automation practice together with the chief data scientist from offerings to client profiles and deliverables, with the creation and delivery of strategic and marketing material to the internal team, clients, and stakeholders.
  • Drove the technical partnership with Intel through multiple R&D projects resulting in thought leadership pieces with thousands of views on our website and presented at multiple conferences.
  • Line managed and mentored juniors to improve their technical and personal skills, define and achieve their OKRs, and become more proactive towards what they enjoy and want to learn.
  • Initiated and coordinated multiple internal initiatives for the team including all hands days, Niko-Niko calendar, surveys, and OKR sessions.
  • Published and oversaw several articles on the company’s blog, from technical topics to event communications in the AI scene.
  • Provided training to global market leaders on the best practices for machine learning on GCP.
Technologies: Google Cloud Platform (GCP), TensorFlow, Consulting, Presales, Recruiting, Management, Thought Leadership, Computer Vision, Propensity Modeling, Deep Learning, Linux, MacOS, XGBoost, Scikit-learn, Keras, Kubeflow, Python, Docker, Machine Learning, Natural Language Processing (NLP), Statistical Methods, Business Planning, Database Design, Web Scraping, Sentiment Analysis, Data Science, Machine Learning Automation, Google Cloud Machine Learning, System Design, Cloud, Networking, Kubernetes, Cloud Dataflow, Pub/Sub, Google Kubernetes Engine (GKE), Cloud Storage, BigQuery, SQL, English, Automation, Google Structured Data, Batch Prediction, Google Cloud Video API, Google Cloud API, APIs, Object Detection, Data Build Tool (dbt), Dask, Google Cloud, Machine Learning Operations (MLOps), Content Writing, Artificial Intelligence (AI), Recommendation Systems, Pandas, Big Data, Algorithms, Deep Neural Networks, Mathematics, OCR, Blogging, Convolutional Neural Networks

Machine Learning Researcher

2017 - 2017
University of Amsterdam
  • Designed the first deep version of NCM in literature, previously considered an impossible task by the community, and further enhanced the classifier with incremental learning and open-set learning techniques.
  • Obtained state-of-the-art accuracy for the NCM and Deep NCM algorithms in multiple scenarios.
  • Performed extensive analysis of state-of-the-art solutions and literature, further progressed by discussions in reading groups beside Ph.D. students.
  • Attended weekly meetings to present deliverables and propose new directions of work.
  • Gathered extensive experience in creating custom models in TensorFlow using graph mode and in Theano, following best practices in the design and finetuning of experiments.
Technologies: Research, Computer Vision, Python, TensorFlow, Deep Learning, Linux, Keras, Machine Learning, Statistical Methods, Mathematics, Web Scraping, Data Science, English, Batch Prediction, Content Writing, Artificial Intelligence (AI), Algorithms, Deep Neural Networks, Convolutional Neural Networks

Machine Learning Intern

2015 - 2015
  • Researched and built a tailored method for automatic polarity tagging of sentences developed in RapidMiner.
  • Guided Wonderflow with the continuance of revenue generating by proposing tailor-built solutions and promoting the business’ technical advancement to investors alongside the founders of the company.
  • Researched and analysed competitors’ techniques and proposed improvements.
  • Reduced the business’ costs by 0.7€/review and optimized the business’ performance by 30 seconds/review, by conceptualizing and developing a new automatic mechanism for the business workflow.
  • Attended events alongside the founders and built relationships with other potential new business partners.
Technologies: Web Scraping, RapidMiner, Natural Language Processing (NLP), Sentiment Analysis, Machine Learning, Statistical Methods, Research, Data Science, English, Batch Prediction, Artificial Intelligence (AI), Algorithms, Mathematics

ML Pipeline Automation with Kubeflow

The client is a global company using Computer Vision to read the emotional responses of people looking at adverts online to understand which marketing campaigns have more impact. They were looking to scale up the efforts of their internal machine learning team to continuously improve their models and solutions by improving the way they create code, run experiments, and track/share results and serve their models.

Marketing Analytics - Brand Propensity Scoring

One of the UK’s largest retailers wanted to predict which of their thousands of brands customers are most likely to buy next. I developed a brand propensity model using TensorFlow and a variety of data sources, and A/B tested through an email campaign promoting new arrivals.

Object Detection for News Articles Retrieval

A fast-growing marketing and advertising agency looking to build a platform for clients and internal teams about where to search for news articles from multiple top newspaper websites. Starting from screenshots of the newspaper's main page, TF Object Detection is used to output stories (bounding boxes) within the page so to localize each news, and Google Cloud APIs are used to extract the origin website, image attached to the article header, text, keywords, and sentiment.


Python, SQL, Snowflake


TensorFlow, Pandas, XGBoost, Scikit-learn, Keras, Google Cloud Video API, Google Cloud API, Dask, PyTorch


Jupyter, Amazon SageMaker, 2Checkout, Cloud Dataflow, Google Kubernetes Engine (GKE), BigQuery, TeamCity, Jenkins, Google Cloud AI, Azure Machine Learning


Data Science, Database Design, Management, Automation, Agile Software Development, DevOps


Google Cloud Platform (GCP), Linux, Docker, MacOS, RapidMiner, Kubernetes, Amazon Web Services (AWS)


Kubeflow, Machine Learning, Computer Vision, Consulting, Thought Leadership, Artificial Intelligence (AI), Recommendation Systems, Algorithms, Deep Neural Networks, Neural Networks, Presales, Sentiment Analysis, Machine Learning Automation, Google Cloud Machine Learning, Object Detection, Deep Learning, Content Writing, Image Processing, Big Data, Technical Writing, Convolutional Neural Networks, OCR, Image Recognition, Writing & Editing, Blogging, Natural Language Processing (NLP), Robotics, Statistical Methods, Mathematics, Business Planning, Economics, Recruiting, Propensity Modeling, Research, Web Scraping, System Design, Cloud, Networking, Pub/Sub, Cloud Storage, English, Google Structured Data, Batch Prediction, APIs, Data Build Tool (dbt), Octopus Deploy, Machine Learning Operations (MLOps), Technical Design, Large Language Models (LLMs), Diffusion Models, Generative Adversarial Networks (GANs)




Google Cloud

2015 - 2017

Master's Degree in AI & Robotics

Sapienza - Rome, Italy

2012 - 2015

Bachelor's Degree in Information Technology

RomaTre - Rome, Italy


Machine Learning Engineering for Production (MLOps)

DeepLearning.AI | via Coursera


Google Cloud Certified Professional Machine Learning Engineer

Google Cloud


Google Cloud Certified Cloud Architect

Google Cloud


Google Cloud Certified Data Engineer

Google Cloud


Cambridge English: Proficiency (CPE)

University of Cambridge

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