Full timeOther

Senior AI Researcher: LLM post-training for hallucination reduction (f/m/d)

AlephalphaHeidelberg🇩🇪Germany

Why this role stands out

Advance your career in AI research by developing cutting-edge techniques to enhance LLM reliability and trustworthiness at a leading European foundation model developer. You'll thrive here if you're passionate about pushing the boundaries of post-training methodologies and contributing to impactful, human-centric AI solutions. Apply to join a dynamic team with access to significant GPU resources and a hybrid work environment.

Quick overview

SeniorityMid Senior

Employment typeFull Time

Work modeHybrid

LocationHeidelberg, Germany

LLMMental HealthPyTorchPythonGenerative AIRAG

Job description

Who we are

Aleph Alpha’s mission is to deliver sovereign, human-centric AI to governmental institutions and large enterprises. To that end, we deliver category-defining AI innovation that enables open, accessible, and trustworthy deployment of GenAI in industrial applications. Our organization develops foundational models and next-generation methods that make it easy and affordable for Aleph Alpha’s customers to increase productivity in development, engineering, logistics, and manufacturing processes.

About the role

We are looking for a (Senior) AI Researcher to join our hallucination reduction capability team to push the boundaries in LLM post-training (SFT and RL).

We're one of the few places in Europe training foundation models from scratch. You will be part of a team of (senior) researchers and PhD interns doing cutting-edge research across pre-, mid- and post-training, grounding and specialized models with hundreds of GPUs at our disposal.

What will you do

Core Research & Development: Design, execute, and scale post-training methodologies (SFT, RL) specifically aimed at eliminating model hallucinations and making them grounded in evidence.
Uncertainty & Reliability: Research and implement techniques in uncertainty estimation, calibration, self-consistency, and self-correction mechanisms to enable models to identify and signal when they “don't know”.
Post-Training Pipelines: Build and optimize high-throughput, distributed fine-tuning and training pipelines across multi-node GPU clusters.
Evaluation & Benchmarking: Design and implement rigorous evaluation methods to benchmark hallucination rates, truthfulness, and reasoning accuracy.
Technical Ownership: Drive research projects independently from initial hypothesis to scalable production implementation and technical reports.

Qualifications

Hands-on experience fine-tuning or post-training LLMs using modern distributed training frameworks.
Track record of research or implementation in at least one of the following domains:
- Hallucination mitigation / truthfulness alignment / grounding in RAG
- Uncertainty estimation, or out-of-distribution detection
- Retrieval-Augmented Generation evaluation / alignment, refusal behavior calibration, or claim verification
Strong programming skills (Python & PyTorch).