Overview
Model Development covers the scientists and engineers who research, design, and optimise machine learning models — from classical ML and deep learning through to large language models, computer vision systems, and recommendation engines. This is where AI innovation happens, and where your competitive advantage often lies.
Roles We Place
- ML Engineers
- Data Scientists
- Research Scientists
- Applied Scientists
- Algorithm Engineers
- NLP Engineers
- Computer Vision Researchers
Tech Stack
PyTorch, TensorFlow, JAX, Hugging Face, scikit-learn, XGBoost, LangChain, OpenAI API, Weights & Biases, MLflow
Typical Hiring Scenarios
Hiring an NLP team to build a domain-specific LLM application — You're building a proprietary LLM for legal, medical, or financial services. You need NLP engineers and research scientists who understand fine-tuning, prompt engineering, retrieval-augmented generation (RAG), and can deliver production-grade language models.
Scaling a recommendation engine team for an e-commerce platform — Your product requires sophisticated recommender systems. You need ML Engineers and Algorithm Engineers who understand collaborative filtering, matrix factorization, deep learning approaches like neural collaborative filtering, and can optimize for both accuracy and latency at scale.
Recruiting a Research Scientist to lead a computer vision R&D function — You're investing in computer vision capabilities (object detection, semantic segmentation, 3D vision, etc.). You need a Research Scientist with a track record of publishing, deep expertise in SOTA models, and the ability to lead a team of CV engineers towards novel solutions.