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

Responsibilities

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.

Qualifications

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.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $162000 - $387600 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;

2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and

3. Exercising sound judgment.

Responsibilities

Responsibilities

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.

Qualifications

Qualifications

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.