Machine Learning Engineer
Pubblicato il 07-09-2026 - Gemmo in Milano
Description
Machine Learning Engineer Positions
We have to fill multiple positions.
Depending on your previous work experience (years in the job market), qualification (Bachelor, Master, Ph.D., Post-doc) you might be offered two types of position.
You have :
- ..no work experience, and a Master / Bachelor -> We offer you an Internship
- ..at least 2 year of work experience OR a Ph.D. / Postdoc -> We offer you a Full-Time Position
About Us
We are a Machine Learning and Computer Vision startup founded in 2020, headquartered in Dublin, Ireland, with an AI Lab in Milan, Italy.
Our expertise spans Machine Learning and Generative AI for financial services and Computer Vision for life sciences.
At Gemmo AI, we build custom AI solutions that combine automation with human insight. We use a modular approach: first we explore the highest-impact opportunities, then we design and deploy tailored solutions, and finally we help improve and maintain them over time.
We believe in responsible, pragmatic AI: systems that integrate into real workflows, provide measurable value,
and remain under your control.
Our Team
- Machine Learning & Engineering: 20 people, including 4 Ph.D.s
- Business & Strategy: 2 people
- Leadership: 2 people
A lean, technical-first team and we're growing. We're looking to add 5 new tech roles before the end of the year
What You’ll Do?
Depending on your profile and interests, you could be placed on one of two flagship tracks:
- Track 1. AI for Financial Services: You'll work on Machine Learning solutions for one of the most data-rich industries in the world. Problems you might tackle include prediction models, document analysis with ML, fine-tuning LLMs for conversational data interfaces, and extracting actionable insights from large-scale datasets.
- Track 2. Computer Vision for Pharma: You'll contribute to Computer Vision pipelines deployed in pharmaceutical environments, think object tracking, behaviour understanding, and solving complex real-world problem