Applied AI Research Engineer – Large Language Models

30 ago - Verona
Equixly

Equixly is an Italian cybersecurity company founded to revolutionise API security through proprietary artificial intelligence and machine learning. Our platform automates API Security Testing, enabling companies to identify deep vulnerabilities rapidly, scalably, and continuously.
We are expanding our AI Team with researchers and engineers who want to work on real, production-grade LLM systems and applied AI challenges.
The role
We are looking for an Applied AI Research Engineer to improve the efficiency and performance of the Large Language Models powering Equixly’s platform.
You will experiment with LLM inference, quantisation, distillation, and low-precision training, turning promising research into validated prototypes and production-ready model improvements. The focus of this role is applied model research rather than DevOps or MLOps. You will report to the Head of AI.
What you will do
- Experiment with and optimise LLM inference using vLLM.
- Evaluate quantisation methods, with particular attention to FP8 and NVFP4, balancing speed, memory usage, and model quality.




- Develop and test model distillation and low-precision training or fine-tuning strategies.
- Benchmark model latency, throughput, memory consumption, and quality using reproducible experiments.
- Collaborate with AI engineers to integrate successful methods into Equixly’s products.

What we are looking for
- PhD in Computer Science, with a research focus on Artificial Intelligence, Machine Learning, Natural Language Processing, or a related area.
- Practical experience training, fine-tuning, evaluating, or optimising Large Language Models.
- Strong Python and PyTorch skills, with experience using Hugging Face Transformers or similar libraries.
- Hands-on knowledge of vLLM or comparable LLM inference frameworks.
- Understanding of model quantisation, particularly FP8, NVFP4, and low-precision model training or fine-tuning.
- Familiarity with knowledge distillation and model compression techniques.
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