Job description
PLEASE NOTE: This position is based in our Swiss HQ in Mendrisio, Switzerland, just 7km over the border from Como, Italy. Mendrisio is easily commutable from Milan, Como, Varese or Lugano and QA provides the train ticket for you! We are also happy to assist with your relocation to this beautiful part of Europe.
About Us
At QA, we believe the future belongs to organisations that are able to learn, master and apply new skills at pace and scale. As the largest tech training company in the UK and the fastest-growing in the US, we partner with 96% of the FTSE and most of the Fortune500. We have served over 4,000 customers and 1+ million learners since 1985.
We believe skills alone aren’t enough but need to be applied back to the business to effect change. We do this through tailored learning programmes that connect learning across an organisation’s siloes, create continuity for learners, and feature collaborative, cohort-based modalities to apply skills at pace and at scale. Our unique end-to-end learning solution draws from deep expertise across apprenticeships, instructor-led training, and self-paced learning.
QA is headquartered in London and New York. Learn more at QA.com.
About the role
We are expanding our dedicated Data & Machine Learning Team and seeking a highly motivated, self-driven ML/AI Engineer. The ideal candidate is passionate about applying critical thinking and creativity to real-world data, developing data-driven applications using machine learning, NLP, and LLMs, and processing both structured and unstructured data to extract insights and support business decisions.
The team works closely with various stakeholders across the organization, including Engineering, Product, Customer Success, and Finance. Additionally, the team actively collaborates with universities and researchers.
The team works on numerous projects that leverage AI and data-driven approaches to enhance the user experience for our customers and optimize internal operations and processes. These projects include:
- AI-powered learning assistants
- AI-powered assistance and automation in content management
- UX personalization through content recommendations
- Skills assessment to measure and address users' knowledge gaps
- Detection of fraudulent activities on the platform
- Extraction of insights through data analysis
The candidate will contribute to existing and new projects, also interacting and collaborating with stakeholders to understand requirements, define interfaces, communicate progress, and ensure successful adoption.
Job requirements
- B.Sc/M.Sc in a quantitative field
- 3+ years of experience in the role
- Experience with Natural Language Processing
- Experience applying Large Language Models (LLMs) in real-world text-based applications (e.g., Retrieval-Augmented Generation (RAG), prompt optimization)
- Proficiency in Python for Data Science (e.g., pandas, numpy/scipy, sklearn) and Machine Learning algorithms (e.g., logistic regression, random forests, etc.)
- Good understanding of statistics fundamentals and exploratory data analysis
- Familiarity with DevOps concepts, including microservices, CI/CD pipelines, and version control; experience in modularizing software logic into components that communicate via APIs; accustomed to peer code reviews.
- Good level of English proficiency, both spoken and written
- Strong debugging and root cause analysis skills
- Ability to manage and interpret ambiguous requirements
- Attention to metrics and demonstrable results
- Interest in building and maintaining services that strongly integrate with a user-facing product
Benefits
- Competitive compensation and a bonus plan
- Four weeks of paid vacation per year (that increases to five weeks after two years with the company!) plus two days off per year to volunteer at your favorite non-profit
- Train subscription paid by the company
- Relocation bonus
Diversity, Equity & Inclusion
We are an equal opportunity employer dedicated to fostering diversity and inclusion. We ensure all applicants are treated fairly, without discrimination based on sex, race, ethnicity, religion, disability, age, gender identity, sexual orientation, veteran status, or any other protected characteristic.
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