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Our mission
Constructor’s mission is to enable all educational organisations to provide high-quality digital education to 10x people with 10x efficiency.
With strong expertise in machine intelligence and data science, Constructor’s all-in-one platform for education and research addresses today’s pressing educational challenges: access inequality, tech clutter, and low engagement of students.
Our headquarters is located in Switzerland, and we also have legal entities in Germany, Bulgaria, Serbia, Turkey, and Singapore
Please send your resume in English only.
We are seeking a talented and experienced Machine Learning Engineer specializing in Large Language Model systems engineering and applied research. The successful candidate will be responsible for implementing and maintaining LLM-based system components, with opportunities to contribute to research initiatives in educational and research domains.
Project Overview
You will be working on production-grade LLM systems that serve research institutions and educational organizations. This includes contributing to scalable inference systems under senior guidance, optimizing model performance, building robust ML pipelines, and contributing to applied research in educational AI. The role emphasizes engineering excellence with research-driven innovation.
Responsibilities:
Required Qualifications
Education & Experience:
Technical Skills:
Preferred Qualifications:
What You’ll Build:
Technical Environment:
What we offer:
Hybrid work in locations with offices (2 days in-office, 3 days remote):
Constructor fosters equal opportunity for people of all backgrounds and identities. We are led by a gender-balanced board committed to building a diverse and inclusive organisation where everyone can become their best self. We do not discriminate based on age, disability, gender identity, sexual orientation, ethnicity, race, religion or belief, parental and family status, or other protected characteristics. We welcome applications from women, men and non-binary candidates of all ethnicities and socio-economic backgrounds. We encourage people belonging to underrepresented groups to apply.