Machine Learning Engineer - E-commerce Merchant Growth (LLM & Agentic Systems)

Description

Team Introduction
We’re the core team building LLM-powered agentic systems that drive and accelerate seller growth across global markets. We bring cutting-edge AI into production at scale — from applied LLMs and multi-agent systems to real-world business impact in one of the fastest-growing e-commerce ecosystems in the world.

We’re looking for brilliant and motivated ML engineers eager to apply their knowledge in machine learning (ML), operations research (OR), data mining, and large-scale intelligent systems to real-world challenges.
If you love building, experimenting, and shaping how AI transforms commerce, we want to talk to you.
Applications are reviewed on a rolling basis — we encourage you to apply early.

Responsibilities
1. Develop and deploy deep learning and LLM-powered systems for merchant operational tools and global e-commerce growth scenarios.
2. Leverage large-scale e-commerce data to power agentic systems that generate actionable insights — from CRM content generation and store decoration to automated email reply and outreach optimization.
3. Use ML models to predict seller performance, identify growth gaps, and provide agent-based recommendations (e.g., campaign design, pricing, and promotions).
4. Collaborate with cross-functional partners (product, data science, operations) to design and deliver 0-to-1 projects that fundamentally reshape how merchants grow, impacting millions of daily sales across key categories (beauty, fashion, health, etc.).
5. Build lead-scoring models, merchant tiering algorithms, outreach optimization systems, and knowledge graphs to enhance onboarding and retention efficiency.
6. Apply data mining and predictive modeling to optimize product pricing, promotion, and traffic allocation strategies.
7. Communicate technical insights effectively to both technical and non-technical stakeholders, fostering a collaborative, data-driven culture.

Requirements

Minimum Qualifications
1. Master’s or PhD degree in Computer Science, Artificial Intelligence, Statistics, Operations Research, or related fields, with experience in data mining or deep learning.
2. Solid understanding of ML and deep learning fundamentals — classification, regression, NLP, and model optimization.
3. Strong programming skills in Python and familiarity with ML frameworks (e.g., PyTorch, TensorFlow).
4. Demonstrated ability to connect algorithms with business impact; highly self-driven with strong ownership and curiosity.
5. 2+ years of experience as an ML Engineer, ideally in NLP, recommendation, marketing, or growth algorithms.
6. Excellent analytical, teamwork, and communication skills.

Preferred Qualifications
1. Experience in applied LLMs, multi-agent systems, or RAG (retrieval-augmented generation) pipelines.
2. Published work in top-tier conferences (KDD, NeurIPS, ICML, SIGIR, WSDM, WWW, AAAI, IJCAI, RecSys, etc.) or success in ML competitions.
3. Hands-on experience in e-commerce or other large-scale, data-intensive production environments.
4. Passion for building agentic systems that drive real-world business outcomes.