Machine Learning Engineer - Recommendations & Personalization

Your Adventure Ahead

• Own recommendation and personalization end-to-end: Build and iterate the ML systems behind ShopBack's personalized shopping experience, recommendations, ranking, user modeling, and CRM intelligence. Own the change end-to-end - dataset, training, offline eval, A/B, rollout - and be measured on offline and online metrics.

• Blend classic ML with modern LLM techniques: Apply fine-tuned open-source models, embedding models, and LLM-based models where they beat classic methods, and know when they don't.

• Ship with evidence: Build evaluation sets and experiment harnesses before shipping models; foster a fast-paced, high-iteration experimentation culture (A/B, interleaving, causal reads).

• Raise the team: Mentor our ML engineers on modern recommendation and LLM practice; your success includes the team's growth, not just your own output.

• Metrics driven: Understand the business and product metrics behind personalization, and drive efforts that move them.

• Handle ambiguity: Navigate loosely defined problems effectively, with or without dedicated Product Manager support.

• Collaboration: Work closely with product, ops, and CRM stakeholders to set and achieve optimal outcomes.

Essentials to Succeed

• 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.