I’m a machine learning engineer and technical lead with 8+ years of experience taking ML solutions from early ideas to real-world production, including work on real-time inference for sensor-heavy applications. I enjoy building robust ML pipelines end-to-end—covering data/model versioning, experimentation workflows, CI/CD, deployment, and performance tuning under real constraints. I’m especially motivated by environments where hardware, software, and ML come together to solve practical problems quickly. Across my roles, I’ve led teams developing detection systems for real-time monitoring, strengthened model generalization through domain adaptation and better experimentation practices, and grown internal ML capabilities from scratch to a scalable, maintainable platform.

Florian Martinez

I’m a machine learning engineer and technical lead with 8+ years of experience taking ML solutions from early ideas to real-world production, including work on real-time inference for sensor-heavy applications. I enjoy building robust ML pipelines end-to-end—covering data/model versioning, experimentation workflows, CI/CD, deployment, and performance tuning under real constraints. I’m especially motivated by environments where hardware, software, and ML come together to solve practical problems quickly. Across my roles, I’ve led teams developing detection systems for real-time monitoring, strengthened model generalization through domain adaptation and better experimentation practices, and grown internal ML capabilities from scratch to a scalable, maintainable platform.

Available to hire

I’m a machine learning engineer and technical lead with 8+ years of experience taking ML solutions from early ideas to real-world production, including work on real-time inference for sensor-heavy applications. I enjoy building robust ML pipelines end-to-end—covering data/model versioning, experimentation workflows, CI/CD, deployment, and performance tuning under real constraints.

I’m especially motivated by environments where hardware, software, and ML come together to solve practical problems quickly. Across my roles, I’ve led teams developing detection systems for real-time monitoring, strengthened model generalization through domain adaptation and better experimentation practices, and grown internal ML capabilities from scratch to a scalable, maintainable platform.

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Experience Level

Expert
Expert
Expert
Expert
Intermediate
Intermediate
Intermediate
Intermediate
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Language

English
Fluent
French
Fluent

Work Experience

Machine Learning Engineer & Technical Lead at eossS A (ex Omnisens SA)
April 1, 2024 - March 31, 2026
Led a 4-engineer ML team developing and deploying real-time threat detection systems. Balanced delivery growth with continuous improvements in model generalization and pipeline robustness. Improved generalization of first-generation models by leveraging multi-customer data, domain adaptation methods, and enhanced experimentation workflows—delivering ~30% performance improvement, reducing deployment variability, and cutting per-customer training/tuning effort by ~50%. Refactored and generalized the ML codebase with an external partner to improve modularity, scalability, and maintainability of data/model pipelines. Collaborated on deep learning approaches (CNN/U-Net) for second-generation models. Continuously improved the ML platform, CI/CD pipelines, and data/model/metric tracking as teams and product complexity grew. Established ML engineering best practices including code standards, documentation, code reviews, data/model versioning, model tracking, and deployment processes. Defined
Machine Learning Engineer at Prysmian
April 1, 2021 - March 31, 2024
Research Engineer at EPFL (Signal Processing Laboratory LTS5)
October 1, 2016 - February 29, 2020
Worked with PhD students on deep learning methods for ultrasound imaging, enabling computationally intensive imaging methods through software and algorithmic optimization. Developed an open-source Python package for ultrasound image reconstruction research, focused on reproducible experiments and consistent comparisons. Implemented reconstruction algorithms accelerated on GPU using C++ and CUDA, achieving real-time performance and enabling integration into a live ultrasound scanner for testing/demos. Designed and implemented GPU-accelerated ultrasound simulations software, achieving ~200x speedup over reference simulations and enabling large-scale training dataset generation.

Education

M.Sc. in Electrical Engineering, Information Technologies at EPFL (École polytechnique fédérale de Lausanne), Signal Processing Laboratory (LTS5)
January 1, 2016 - January 1, 2016
B.Sc. in Electrical Engineering at EPFL (École polytechnique fédérale de Lausanne)
January 1, 2014 - January 1, 2014

Qualifications

Add your qualifications or awards here.

Industry Experience

Software & Internet, Computers & Electronics

Experience Level

Expert
Expert
Expert
Expert
Intermediate
Intermediate
Intermediate
Intermediate
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