Staff Machine Learning Engineer

Build machine learning at internet scale and help power the performance of Coupang’s advertising platform. In the Coupang Media Group (CMG) Ads Quality & Ranking organization, you will design and deploy models, ranking systems, and optimization capabilities that improve ad relevance and engagement. This role also supports a transition of the ad quality stack toward enterprise-grade ML infrastructure, while extending full-stack ad tech solutions.

Responsibilities

• Architect, build, and deploy large-scale machine learning and deep learning models to improve ad targeting, relevance, ranking, and user engagement.
• Develop advanced techniques for automated mechanisms, including auto-bidding, query expansion, and advertiser ROI optimization models.
• Design systems that produce actionable suggestions and insights for advertisers, helping them find growth opportunities within the Coupang marketplace.
• Architect and evolve an end-to-end ML platform to serve high-throughput, low-latency ad requests at internet scale.
• Maintain high standards for code quality, system reliability, and automated testing across complex distributed ML pipelines.
• Lead large technical initiatives across multiple engineering teams, aligning technical roadmaps with business objectives.
• Provide technical guidance, code reviews, and architectural mentorship for mid-level and senior engineers.
• Collaborate with Product Management, Data Science, and Business Leadership to deliver next-generation advertising features.

Requirements

• Bachelor’s degree in computer science, electrical engineering, mathematics, statistics, or a closely related field.
• At least 4 years of professional experience in applied machine learning.
• Experience with machine learning, deep learning, and statistical modeling.
• Proficiency in Python and/or Java, with experience building production-grade ML systems.

Technology Stack

Python, Java, TensorFlow, PyTorch, Scikitlearn, Keras, XGBoost, LightGMB, H2o.ai, MLflow, Kubeflow, Weights & Biases, Amazon SageMaker, AWS, Google Cloud Platform, Vertex AI, BigQuery, Vertex AI, SageMaker

Preferred Qualifications

• Master’s or PhD in a relevant technical field.
• Experience with search systems, information retrieval, or recommendation engines.
• Experience with LLMs, embeddings, and vector search technologies.
• Experience with cloud platforms such as AWS or Google Cloud Platform, including Vertex AI, BigQuery, or SageMaker.
• Experience in startup or high-growth environments.
• Proven ability to lead cross-functional teams and deliver results in a multicultural, global organization.
• Hands-on experience with modern ML frameworks such as TensorFlow, PyTorch, Scikitlearn, Keras, XGBoost, LightGMB, and H2o.ai.
• Experience with ML lifecycle tools such as MLflow, Kubeflow, Weights & Biases, or Amazon SageMaker.
• Excellent communication skills for explaining complex technical concepts to technical and non-technical stakeholders.
• Demonstrated ability to work independently and manage ambiguity in fast-paced environments.

Compensation & Location

Location: Mountain View, CA (onsite)

Base pay range: USD $164,000 to $282,000 per year, based on geographic market.

Benefits

• Annual bonus: 0-20% of base salary
• Medical/Dental/Vision/Life and AD&D insurance
• Flexible Spending Accounts (FSA) and Health Savings Account (HSA)
• Long-term/Short-term Disability
• Employee Assistance Program (EAP)
• 401K Plan with Company Match
• Paid Time Off (PTO): 18-21 days per year based on tenure
• 12 Paid Holidays
• Paid Parental leave
• Pre-tax commuter benefits
• Free Electric Car Charging Station

Recruitment Process

• Application Review - Job Fit Interview - Focus Interview - Offer

The exact process may vary by role and can change due to scheduling or other circumstances. Interview schedules and results are communicated to the email provided at application.

Additional Details

• This posting may close before the stated end date if all openings are filled.
• Offer rescission may occur if false information is submitted during the application process.
• Candidates eligible for employment protection (for example, recipients of veteran’s benefits or the disabled) may receive preferential treatment under applicable laws.