Senior AI Researcher: LLM post-training for hallucination reduction (f/m/d)
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Before you apply
LocationHeidelberg
Work modehybrid
Typefull-time
SenioritySenior
DepartmentResearch
Company size4+ people
PostedOct 14, 2026 · 3w ago
Verified live3d ago
At a glanceSummarised by Seekless from the posting.
Hands-on experience fine-tuning or post-training LLMs using modern distributed training frameworks
Track record of research or implementation in hallucination mitigation / truthfulness alignment / grounding in RAG
Track record of research or implementation in uncertainty estimation, or out-of-distribution detection
Track record of research or implementation in Retrieval-Augmented Generation evaluation / alignment, refusal behavior calibration, or claim verification
Strong programming skills (Python & PyTorch)
Skills
Python
PyTorch
Benefits
Vacation
Wellness
Mental health support
Pension plan
Transportation ticket
Equipment budget
Flexible hours
Bike lease
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
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Core Research & Development: Design, execute, and scale post-training methodologies (SFT, RL) specifically aimed at eliminating model hallucinations and making them grounded in evidence.
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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”.
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Post-Training Pipelines: Build and optimize high-throughput, distributed fine-tuning and training pipelines across multi-node GPU clusters.
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Evaluation & Benchmarking: Design and implement rigorous evaluation methods to benchmark hallucination rates, truthfulness, and reasoning accuracy.
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Technical Ownership: Drive research projects independently from initial hypothesis to scalable production implementation and technical reports.
Qualifications
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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).
Compensation and Benefits
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Become part of an AI revolution!
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30 days of paid vacation
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Access to a variety of fitness & wellness offerings via Wellhub
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Mental health support through nilo.health
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Substantially subsidized company pension plan for your future security
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Subsidized Germany-wide transportation ticket
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Budget for additional technical equipment
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Flexible working hours for better work-life balance and hybrid working model
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JobRad® Bike Lease