Machine Learning Engineer - Recommendations & Personalization

Has shipped and iterated recommendation / personalization / ranking or search systems serving millions of users, and can walk through what moved the metrics, what didn't, and why (typically 2+ years of industrial ML experience).

Strong grounding in retrieval and ranking modeling, embeddings, and online experimentation.

Hands-on fine-tuning of open-source models/LLMs (SFT / LoRA / DPO) applied to ranking, personalization, or user modeling, and the judgment of when classic methods win.

Builds evaluation sets and harnesses as a default step, not an afterthought.

Understands dataset licensing and provenance for commercial use.

Solid MLOps fundamentals: data pipelines, productionisation, monitoring, and GPU cost awareness.

Comfortable in a batch data stack — Spark or equivalent, a scheduler(e.g. Airflow), cloud training and serving (e.g. AWS SageMaker). You’d own the model through ingestion, training, serving, monitoring, retraining and rollback.

Strong Python and PyTorch; familiarity with the Hugging Face ecosystem (transformers / PEFT / TRL) and modern inference stacks (e.g. vLLM) is a plus.

Uses agentic AI tools as a daily driver for engineering work, and can show how they changed your workflow.

Education in a quantitative field such as Computer Science, Statistics, or Mathematics, or equivalent practical depth.

Strong desire to solve tough problems with scientific rigour at scale, and to get results early and iterate.