Machine Learning Engineer Intern

About us

Waiv, a spin-out of Owkin, exists to catalyze precision medicine with clinical-grade, AI-powered tests that accelerate biomarker discovery and detection, outcome prediction, and treatment-response assessment across oncology. Built on over seven years of validated medical-AI innovation and one of Europe’s largest multi-institutional data networks, we deliver precise, accurate, and fast analyses that support treatment decisions. Through our industry-leading tech and AI stack, and clinically validated, already-deployed AI tests, such as RlapsRisk BC, we unlock morphological insights and future-proof both the clinical diagnostic workflow and pharmaceutical R&D pipelines, to ultimately bring scalable precision medicine to patients worldwide. www.wearewaiv.com

What we offer

• Swile “tickets restaurants”
• Commuting allowance and/or biking allowance
• Friendly and informal working environment
• Opportunity to work with an international team with high technical and scientific backgrounds

About the role: Machine Learning Engineer Intern

As a Machine Learning Engineer Intern, you will contribute to advancing AI in life sciences and help build the world's leading AI diagnostic product for clinical routine. You will support the design, training, and integration of cutting-edge models working with medical images, particularly histology slides, which are cornerstone to cancer diagnosis. You'll work at the intersection of AI research and production systems, contributing to robust ML pipelines for medical data analysis. You'll gain hands-on experience balancing applied research with practical engineering, helping shape pipeline to analyze data, and model development. You'll work directly with the Machine Learning Engineering team under the supervision of an ML Engineer, and collaborate with the Data Science team.

In particular, you will:

• Collaborate within a multi-disciplinary team of data scientists, machine learning engineers, product managers, and software engineers.
• Support the research, design, and development of machine learning models tailored to complex and large-scale imaging datasets.
• Contribute to the development of our proprietary ML libraries & workflows in collaboration with the R&D team.
• Contribute to the development and validation of new AI models.
• Participate in the implementation of industrial-scale AI pipelines for medical data analysis, ensuring robustness across production environments.
• Assist in establishing evaluation and monitoring methodologies to ensure model performance, reliability, and interpretability in production.
• Help translate state-of-the-art research into practical solutions.
• You will specifically contribute to develop tools to help make our models more robust.

About you

Required qualifications / experience:

• Strong scientific curiosity and creativity with an applied mindset.
• Interest in machine learning engineering and development.
• Basic understanding of deep learning architectures (foundation models, transformers, vision models, etc.).
• Familiarity with basic evaluation metrics of deep learning models.
• Having worked with Medical Images is a plus
• Good programming skills (Python, PyTorch or TensorFlow).
• Excellent communication and collaboration skills.
• Fluent English, both written and spoken.
• Currently pursuing a Master's degree in Computer Science, Software Engineering, Machine Learning, Data Science, or related field.

Stack:

Python, PyTorch, AWS

Recruitment Process & Security

Please attach a CV in English. Waiv is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, sex, gender, sexual orientation, age, color, religion, national origin, protected veteran status or on the basis of disability. Waiv is a great place to work. Unfortunately, being a coveted workplace means we are vulnerable to recruitment phishing scams. We urge all job seekers and candidates to be wary of potential scams. Most of these have individuals posing as representatives of prominent companies, including Waiv, with the aim of obtaining personal, sensitive, or financial information from applicants. These scams prey upon an individual’s desire to obtain a job and can sometimes “feel” like a genuine recruitment process. Some red flags are identified below. Should you encounter a recruitment process that claims to be for Waiv but is not consistent with the below, please do not provide any personal or financial information:

• Legitimate Waiv recruitment processes include communication with candidates through recognized professional networks, such as LinkedIn. However, further communication is always through an official Waiv email address (from the @wearewaiv.com domain), over the phone or though Recruitment platforms (Welcome To The Jungle);
• Legitimate Waiv recruiters will not solicit personal data from candidates during the application phase including, but not limited to, date of birth, social security numbers, or bank account information;
• Legitimate Waiv interviews may be conducted over the phone, in person, or via an approved enterprise videoconferencing service (such as Google Meets). They will never occur via Signal, Telegram or Messenger;
• Legitimate Waiv offers of employment are based on merit and only extended once a candidate has interviewed with members of the hiring team. Offers will be extended both verbally and in written format. Waiv may request some personal information to initiate the hiring process, but this will be through protected means.

If you think that you have been a victim of fraud, Check the identity of recruiters on LinkedIn Check the existence of the position on our website Notify Waiv recruitment unit contact the following authorities: [FR] https://www.internet-signalement.gouv.fr/ [UK] https://www.actionfraud.police.uk/reporting-fraud-and-cyber-crime [US] https://www.ftccomplaintassistant.gov/#crnt&panel1-1

Key skills/competency

• Machine Learning
• Deep Learning
• AI
• Medical Imaging
• Histology
• ML Pipelines
• Python
• PyTorch
• AWS
• Data Analysis