About the Role
Join Aevum Zenth’s Advanced AI Research Lab as an NLP Research Engineer, where you’ll help architect the next generation of foundational language models and enterprise AI systems. Our multidivisional structure means your work will directly impact 400+ subsidiaries, from automating complex supply chain negotiations to powering next-gen healthcare diagnostics and real-time financial market analysis.
We’re looking for researchers and engineers who thrive at the intersection of academic rigor and production-scale deployment. You’ll collaborate with world-class scientists, data engineers, and divisional product teams to ship systems that handle trillions of tokens while maintaining strict safety, efficiency, and multimodal alignment standards.
Key Responsibilities
- Design, train, and evaluate large language models (LLMs) and specialized NLP architectures for enterprise applications across finance, healthcare, logistics, and aerospace.
- Develop novel training objectives, RLHF/RLAIF pipelines, and alignment techniques to improve reasoning, factuality, and domain adaptation.
- Build highly optimized inference and serving infrastructure using PyTorch, JAX, and distributed training frameworks (Megatron, DeepSpeed, vLLM).
- Partner with divisional engineering teams to integrate research prototypes into production systems, ensuring scalability and low-latency deployment.
- Author technical publications, present at top-tier AI conferences, and contribute to open-source tooling where strategically appropriate.
- Mentor junior researchers and engineers while fostering a culture of scientific excellence and cross-functional collaboration.
Qualifications
- Ph.D. or M.S. in Computer Science, Computational Linguistics, or a closely related field, with 3+ years of professional experience in NLP/AI research.
- Proven track record of publishing in top-tier venues (ACL, EMNLP, NeurIPS, ICML, ICLR) and/or shipping production-grade AI systems.
- Deep expertise in transformer architectures, pretraining/finetuning strategies, prompt engineering, and retrieval-augmented generation (RAG).
- Proficiency in Python, CUDA optimization, and modern ML frameworks (PyTorch, JAX, Hugging Face ecosystem).
- Experience with distributed training, model quantization, pruning, and serving optimization at scale.
- Strong communication skills and the ability to translate complex research concepts for technical and non-technical stakeholders across diverse industries.
Benefits & Perks
Application Process
We review applications on a rolling basis. Selected candidates will complete a technical research review, a system design interview, and a cultural alignment conversation with the Advanced AI Lab directors. We are committed to building a diverse and inclusive team and encourage applications from all backgrounds.