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ML Research Intern in Agentic Runtime Systems

Company
constructortech
Listed on
Arbeitnow
Location
Bremen
Employment type
Internship
Team
Internship
Posted
Nov 4, 2026

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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.

Please send your resume in English only.

Interns are NOT eligible for equipment and other benefits as the position is temporary.

The Reality

The frontier models are converging into commodities; the engine is no longer the differentiator. The scarcity in 2026 is not the model.

We are not looking for people to fine-tune Llama on toy datasets or write "better prompts." We are building the runtime systems that turn probabilistic token generation into reliable workers that finish 50-step tasks without spiraling. We are solving the math of multi-step reliability through engineering, not wishful thinking.

What You Will Solve

You will work on the Agentic Harness, treating the LLM as a component within a larger, deterministic control system. Your research will focus on:

Who We Are Looking For

We are looking for systems thinkers who happen to know ML, not the other way around.

What We Offer For Interns:

What We Offer

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.

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