Machine Learning Engineer / Researcher (IR-556)

About the role

This role sits within a focused research initiative dedicated to adapting small language models (SLMs) to specialized, high-value domain applications.

What you'll do

• Own the Model Adaptation Lifecycle: Drive end-to-end adaptation of SLMs to specialized domain tasks, choosing the right base models, training strategies
• Apply Efficient Fine-Tuning: Implement parameter-efficient fine-tuning (PEFT) methods with a mathematically grounded understanding of low-rank adaptation (LoRA, QLoRA
• Design Functional Evaluations: Build task-specific benchmarks grounded in functional, executable correctness rather than loss curves or perplexity alone
• Deploy & Optimize Serving: Stand up and optimize inference infrastructure across GPU, CPU, and edge targets using frameworks such as vLLM, TGI, Ollama, llama.cpp
• Pipeline Reproducibility & Research Execution: Break target tasks into milestones with clear criteria, maintaining reproducible pipelines with data/config versioning

What you need

• 3–6 yrs of experience
• Machine learning
• Python
• PyTorch
• LLMs
• Any graduate

Good to have

• SQL