AI Engineer Intern

TEOXANE Laboratories; Genf; On-site; CHF 20,000 - 31,000; Part time

Job summary

Teoxane Laboratories in Geneva, Switzerland, offers a 6-month internship in the IT department within the AI Lab. You will build an image processing pipeline to measure skin color from clinical photos and develop a probabilistic model with quantified uncertainty.

Responsibilities include quality gating, variance analysis, cross-system calibration, and contributing to Noesis and LightRAG initiatives. Strong Python and colorimetry interest are essential.

Qualifications

• Ongoing Master’s in Data Science, Applied Statistics, Engineering, or a related field.
• Strong Python: NumPy, pandas, scikit-learn, and image processing (OpenCV, Pillow).
• Solid applied statistics: variance components, uncertainty quantification, calibration.
• Familiarity with PyTorch; interest in colorimetry (CIELAB), LLMs and RAG is an asset.
• Curious, rigorous, autonomous; works well independently and in a cross-functional team.

Responsibilities

• Build an image processing pipeline sampling skin color at multiple facial locations with automated quality gating.
• Quantify measurement precision: variance decomposition across subject, site, imaging system, viewing angle and visit.
• Characterize and correct differences between imaging systems.
• Develop a calibrated probabilistic model estimating Fitzpatrick skin type with explicit uncertainty.
• Run robustness and fairness checks across the measured skin tone range.
• Contribute to other AI Lab initiatives and document methodology, results, and code.

Skills

Python

Image processing

Statistics

Colorimetry

Cross-functional teamwork

Education

Master's in Data Science

Tools

OpenCV

Pillow

NumPy

pandas

scikit-learn

PyTorch

Job description

Teoxane Laboratories were established in Geneva, Switzerland, in 2003. They are specialized in the design and manufacturing of hyaluronic acid-based dermal fillers and dermocosmetics. Teoxane strength is to offer a global patient care. Using its products, practitioners are able to fill all kinds of wrinkles, from the finest to the deepest, to restore youthful volume to the face, and to treat the neck and décolleté, with natural results. The strength of Teoxane is also its human size and independence. Teoxane has a total freedom of thoughts, action and innovation which makes the company unique.

In the frame of the growth of our business, we are looking for an:

Contract: 6 months (start date February 2027)

Within the IT department, as part of the AI Lab, this internship contributes to Project ChromaDerm: measuring skin tone objectively and reproducibly from clinical photographs. Teoxane holds a large photographic dataset acquired across multiple studies, sites and imaging systems. The internship builds the measurement layer first – how precisely skin color can be read from these images – and then the predictive model on top of it.

Main activities

• Build an image processing pipeline sampling skin color at multiple facial locations, with automated quality gating (facial segmentation, rejection of specular highlights, shadows and occlusions),
• Quantify measurement precision: variance decomposition across subject, site, imaging system, viewing angle and visit; repeatability and test–retest analysis,
• Characterize and correct or statistically account for differences between imaging systems,
• Develop a calibrated probabilistic model estimating Fitzpatrick skin type with explicit uncertainty, and a logic for flagging discordant cases for human review,
• Run pre-specified robustness and fairness checks across the measured skin tone range,
• In a second phase, as a support project, contribute to other AI Lab initiatives such as Noesis (Teoxane’s internal AI assistant) and LightRAG-based document processing,
• Document methodology, results and code to ensure project continuity.

Your profile

• Ongoing Master’s in Data Science, Applied Statistics, Engineering, or a related field,
• Strong Python: NumPy, pandas, scikit-learn, and image processing (OpenCV, Pillow),
• Solid applied statistics: variance components, uncertainty quantification, calibration,
• Familiarity with PyTorch; interest in colorimetry (CIELAB), LLMs and RAG is an asset,
• Curious, rigorous, autonomous; works well independently and in a cross-functional team,