Senior LLM Scientist for Edge & Cloud GenAI

SQUAD; United States; Remote; USD 140,000 - 210,000; Full time

Benefits offered by this job

Remote work within Ukraine

Medical insurance from day one

21 paid vacation days per year

Educational leaves

Free English classes

Job summary

SQUAD is seeking an experienced Applied Scientist with a strong background in Large Language Models to develop high-performance Generative AI features across Cloud and Edge environments. You will drive the transition from research to production by optimizing local inference through model compression and quantization for private, real-time Edge performance.

Your role includes engineering scalable RAG architectures, multi-agent systems for Cloud deployment, and end-to-end research lifecycle tasks

Qualifications

• 3+ years of commercial experience in Machine Learning, focusing on NLP/LLM.
• Strong programming experience in Python and ML libraries.
• Hands-on experience deploying LLMs to production (PEFT/LoRA, RAG, or agentic frameworks).

Responsibilities

• Design and implement prompt orchestration, fine-tuning (SFT/RLHF/DPO), and autonomous agent workflows.
• Curate large-scale training data from text, image, and audio sources.
• Identify patterns in model hallucinations and visualize evaluation metrics.
• Tune hyperparameters and improve inference speed/accuracy via PEFT and prompt engineering.
• Collaborate with Product and Data Engineering teams to integrate LLM features.
• Track progress using standard benchmarks and internal KPIs.
• Stay at the forefront of transformer variants and production-ready techniques.
• Mentor junior team members to elevate expertise.

Skills

NLP/LLM expertise

Python

PyTorch

Hugging Face Transformers

PEFT/LoRA

Reinforcement Learning

Tools

PyTorch

Hugging Face Transformers

PEFT

ONNX/Triton

Job description

SQUAD is seeking an experienced Applied Scientist with a strong background in Large Language Models to develop high-performance Generative AI features across Cloud and Edge environments. You will drive the transition from research to production by optimizing local inference through model compression and quantization for private, real-time Edge performance.

Your role includes engineering scalable RAG architectures, multi-agent systems for Cloud deployment, and end-to-end research lifecycle tasks