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AlephAlpha

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

Heidelberg; BerlinHybrid · Germany
Senior · FullTime · Research
POSTED 14 OCT
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Role
25 days opentrue age
Posted 14 Oct
Pay
€77k–€155kno pay posted
Market p25–p75
€77k–€155k
Germany · n=7 · 365d to 6 Nov
CompanyAlephAlpha →
4open roles
Opened · 30d
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Closed · 30d
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Market
1,510open · Senior ML / AI
All ml / ai
6,683open
All tracked boards · live 8 Nov · n=1,510
Node.jsPythonPyTorchLLMs

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).

Compensation and Benefits

Become part of an AI revolution!

30 days of paid vacation

Access to a variety of fitness & wellness offerings via Wellhub

Mental health support through nilo.health

Substantially subsidized company pension plan for your future security

Subsidized Germany-wide transportation ticket

Budget for additional technical equipment

Flexible working hours for better work-life balance and hybrid working model

JobRad® Bike Lease

Source · AlephAlphaFirst seen 4 NovOriginal ↗