Machine Learning Engineer - Recommendations & Personalization

Shopback-2

Jakarta Pusat

On-site

IDR 400,000,000 - 700,000,000

Full time

14 days+

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Benefits offered by this job

Career growth opportunities

Be part of a winning team

Competitive compensation

Candid, open, collaborative culture

Job summary

ShopBack Group is seeking an experienced ML Engineer to own the recommendation and personalization stack, from dataset and training to offline/online evaluation. You will combine classic ranking with LLM-enhanced methods, shipping models with rigorous evaluation and metrics-driven outcomes.

You will mentor ML engineers, collaborate with product and CRM, and champion MLOps and robust experimentation in a fast-paced environment. This role is based in Jakarta and offers growth opportunities.

Qualifications

Shipped and iterated recommendation/personalization/ranking systems.
Strong grounding in retrieval, embeddings, and online experimentation.
Hands-on fine-tuning of open-source models/LLMs (SFT/LoRA/DPO).
Build evaluation sets and harnesses for rapid experimentation.
Understand dataset licensing and provenance for commercial use.
Solid MLOps fundamentals: data pipelines, productionisation, monitoring.
Proficient in Python and PyTorch; familiar with Hugging Face tech.
Able to use agentic AI tools to improve engineering workflows.
Quantitative degree with ability to solve tough problems at scale.

Responsibilities

Own recommendation and personalization end-to-end with measurable impact.
Blend classic ML with modern LLM techniques where beneficial.
Ship with evidence: develop evaluation sets and experiments before release.
Mentor ML engineers on ranking and recommender practices.
Drive business metrics through product-aware ML initiatives.
Navigate ambiguity and collaborate with product, ops, and CRM.

Skills

Recommendation systems

Ranking models

Online experimentation

Python

PyTorch

MLOps

Data pipelines

Model evaluation

LLM fine-tuning

Education

Bachelor's in CS/Math/Statistics

Tools

Spark

Airflow

AWS SageMaker

Transformers

PEFT/LoRA

TRL

vLLM

Job description

Our Journey

The ShopBack Group is Asia-Pacific’s leading shopping, rewards, and payments platform, serving over 20 million active members across 13 markets. In 2025, the Group continued its global growth with its expansion into North America. Driven by the vision to make every day more rewarding, ShopBack is dedicated to saving members money and time, and delivering delight every day. The platform also enables merchants and brands to engage with their members in a cost-effective manner. Founded in 2014, ShopBack now powers over US$5.5 billion in annual sales for over 20,000 online and in-store partners, and has rewarded shoppers with more than US$900 million (over S$1 billion) in Cashback to date. Through its innovative offerings, ShopBack continues to create value for both members and merchants. Notably, its payment solution, ShopBack Pay, offers members a convenient and rewarding payment option at checkout.

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.

ShopBacker Traits

Agency - We take ownership and act, rather than waiting for permission. When something's blocking progress, we find a way through it and follow through until it's 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.

What's in it for ShopBackers