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# Founding ML Researcher

- **Company:** Base Compute
- **Location:** Berlin, Germany
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Nvidia CUDA, Reinforcement Learning, Large Language Models, Machine Learning Operations
- **Published:** August 12, 2026
- **Apply:** https://de.indeed.com/viewjob?jk=d59e7d4a7eda2cd8

## About the Role

* PhD in ML or equivalent industry research experience
 * Deep understanding of LLM architectures and the principles of AI inference
 * Expertise in a relevant topic, such as speculative decoding, quantization theory, model distillation, reinforcement learning
 * A track record of producing results that people build on: research papers, open-source projects or blog posts that prove out novel ideas
 * Good communication: the ability to explain complex ideas simply, give honest feedback and document findings in a reproducible way
 * Nice-to-haves:
 + Familiarity with GPU and accelerator architectures and kernel optimization (CUDA, ROCm, Metal, Triton, etc.)
 + Experience deploying models under on-device constraints (memory bandwidth, latency budgets, and thermal and power ceilings)

## Description

We're looking for a Founding ML Researcher to work at the frontier of on-device AI. This role is for someone who identifies problems and potentials, designs and executes experiments and derives insights that translate into real-world performance., * Inference research: Identifying and validating new approaches to on-device efficiency, including speculative decoding variants, novel quantization schemes and entirely new techniques yet to be discovered

 * Model routing research: Building the intelligence that decides how requests are served between on-device vs. frontier API models
 * Autoresearch pipelines: Designing systems that can autonomously explore, hypothesize and evaluate research ideas that accelerate our R&D loop
 * Evaluations and benchmarks: Developing rigorous evals that measure performance in the real world, outside of clean academic settings