Senior Applied Research Engineer - Model Compression
Develop new compression methods for large language models, improve and extend structural pruning algorithm, and work on model retraining pipeline. Requires PhD in computer science or equivalent, published work on quantization, pruning, or LLM training, and production-grade Python code.
Key Highlights
Key Responsibilities
Technical Skills Required
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Job Description
Ora Computing · Vienna · Full-time
We compress large language models (LLMs). Our information-theoretic structural pruning and quantization algorithm shrinks model footprints by over 80% without retraining, in hours rather than weeks.
This is an applied research role. You'll develop new compression methods and ship them, not write papers about them. The cycle is short: read the literature, prototype, benchmark on real models, integrate into our pipeline, iterate with customers running compressed models in production.
You'll own significant technical scope from day one. Expect to work across the stack: pruning algorithms, quantization, evaluation infrastructure, and the production code that customers actually use.
- Improving and extending our structural pruning algorithm to new architectures (MoE, multimodal, vision-language)
- Combining pruning with quantization (NVFP4/FP8/INT4, sub-4 bit mixed precision) in our compression pipeline
- Expanding and improving our model retraining pipeline (SFT, GKD, DPO, GRPO)
- Compressing customer models (Llama, Qwen, Gemma, and proprietary fine-tunes) for cloud and edge deployment
- Hardware-aware optimization for different accelerator targets (A100/H100/B300 and edge hardware)
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- PhD in computer science, machine learning, or equivalent
- Published work on quantization, pruning, or LLM training
- Production-grade Python code (not just Jupyter notebooks). You write code others can read and run
- Experience taking a method from paper to a working system on real models
- Comfort in working with LLMs, GPUs, and evaluating benchmarks
- You ship. You finish things.
- Open-source contributions to ML infrastructure (vLLM, llama.cpp, transformers, TensorRT-LLM, bitsandbytes, GPTQ/AWQ implementations)
- Experience with MoE architectures or multimodal models (Qwen Omni)
- Background in kernel optimization
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- Vienna-based. Hybrid or fully remote
- Working language is English
- We sponsor visas and support relocation
- Compensation: €70-120k base + equity. Austrian minimum disclosed per Kollektivvertrag: €43,456/year
- We don't require writing publications, but we support presenting work at venues when it fits the company and the project
Send CV, list of representative papers, and other relevant info to info@oracomputing.com. Tell us in two paragraphs what you'd want to work on at Ora and why. We respond within a week.
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