Senior AI Researcher: LLM post-training for hallucination reduction (f/m/d)
Quick Summary
Hallucination mitigation / truthfulness alignment / grounding in RAG Uncertainty estimation, or out-of-distribution detection Retrieval-Augmented Generation evaluation / alignment,
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
~1 min readWe 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.
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.
Requirements
~1 min readHands-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).
What We Offer
~1 min readLocation & Eligibility
Listing Details
- Posted
- October 14, 2026
- First seen
- October 14, 2026
- Last seen
- October 14, 2026
Posting Health
- Days active
- 0
- Repost count
- 0
- Trust Level
- 60%
- Scored at
- October 14, 2026
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