Machine Learning Engineer Recommendations & Personalization
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Job Summary
Own recommendation and personalization end-to-end: Build and iterate the ML systems behind ShopBacks 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 dont.
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 teams 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.
Has shipped and iterated recommendation / personalization / ranking or search systems serving millions of users and can walk through what moved the metrics what didnt 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). Youd 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.
- Agency - We take ownership and act rather than waiting for permission. When somethings blocking progress we find a way through it and follow through until its done.
- Judgement- We aim for high-impact decisions not just easy wins and we put the bigger picture ahead of individual interests. That means moving quickly and confidently while staying thoughtful about when a call really matters.
- Learning Velocity - We pick up new skills fast and let go of old habits just as quickly when something better comes along. We benchmark ourselves against the best and keep raising our own bar.
- Tenacity - We stay in it when things get hard keeping a level head under pressure. We debate openly before deciding then commit fully and support each other along the way.
- Career growth opportunities to take on greater challenges that help you realise your ambitions.
- Be part of a winning team on a journey to global scale.
- Competitive compensation based on performance.
- Candid open and collaborative culture where feedback is valued.
Required Experience:
IC
About Company
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