Artificial Intelligence (AI)/Machine Learning Engineer

About Logitech & the Team

Logitech is the established market leader in video communication solutions for consumers and enterprise users. The Video AI/ML Technology Development Team creates cutting‑edge AI/ML algorithms and video pipelines for edge peripherals and host devices, focusing on consumer webcam applications. The team is based at the EPFL Innovation Park in Lausanne, Switzerland, and collaborates closely with software and hardware groups in North America and Asia.

Responsibilities

• Develop AI/ML algorithms and video pipelines across the entire model lifecycle: design, training at scale, optimization, porting, and deployment to embedded and hosted target platforms.
• Work with a multidisciplinary team to champion best practices, turning cutting‑edge research into robust, innovative solutions that define the future of human‑machine interaction.

Minimum Qualifications

• Minimum 8 years of relevant work and/or academic experience in algorithm development (signal processing and AI/ML) with an emphasis on live video.
• Hands‑on experience with the latest AI/ML (deep learning) techniques, models, and toolchains—from architecture to training, evaluation, optimization, and porting to embedded systems (Linux/Android) and client platforms (macOS/Windows).
• Experience training and scaling large models on distributed computing clusters (e.g., AWS, GCP, or institutional HPC clusters).
• Familiarity with data engineering collection, generation, augmentation, and governance.
• Experience with software development best practices (SDLC) in embedded systems and host clients.
• Ability to collaborate in a cross‑functional team spanning engineering, business, and design.
• Keen interest in human‑machine interactions.
• Pragmatic, innovative, curious & autonomous.
• Organized with strong communication skills.

Skills

• Design, training, evaluation, and optimization of deep neural networks (e.g., CNNs, RNNs, GANs); transformers and LLMs are a plus.
• Knowledge of loss modeling, multimodal training, supervised, unsupervised, and self‑supervised learning.
• Deep learning frameworks such as PyTorch, TensorFlow, or TinyML, and ONNX model representation.
• Familiarity with cloud computing platforms or HPC environments for large‑scale model training.
• Proficiency in general‑purpose programming languages: Python, C/C++, C#, and Java.
• Software development on embedded platforms (e.g., ARM) and embedded AI; SDLC experience is a plus.

Education

• MSc in Robotics, Computer Science, Computational Sciences, Machine Learning, Data Science, or related fields.
• PhD in related fields is a plus.