酷澎
Staff Machine Learning Engineer
Staff Machine Learning Engineer
发布于 大约 20 小时前普通员工/个人贡献者
Mountain View, USA
中级经验
全职员工
仅现场办公
本科
PyTorch
TensorFlow
LLM
MLflow
Machine Learning
AI 估算 · 91k–166k
根据JD明确薪资范围152K-277K美元年薪，按汇率7.2估算，作为Staff工程师薪酬竞争力强。
职位详情
关于这个职位
This role focuses on building scalable ML platforms for search and recommendation at Coupang, handling billions of data points and serving real-time models. You will design and implement large-scale systems using state-of-the-art techniques like transformers and LLMs, contributing directly to business growth and customer experience.
最低要求
Bachelor's degree in computer science, electrical engineering, mathematics, statistics or closely related fields
years of professional experience in applied machine learning
Experience in machine learning, deep learning, and statistical modeling
Proficiency in Python and/or Java, with experience in building production grade ML systems
工作职责
Design features and build large-scale machine learning models and systems to improve ad targeting, relevance, ranking, and engagement
Design and implement large-scale ML systems for search ranking, semantic retrieval, query understanding, and personalized product discovery using state-of-the-art techniques such as transformer-based models, contrastive learning, and vector search
Drive innovation in search relevance and user intent modeling using large language models (LLMs), embedding-based retrieval, and multi-modal learning
Build and optimize ML pipelines using tools such as Apache Spark, Airflow, Kubeflow, and MLflow, ensuring reproducibility, scalability, and operational excellence
Define and track key performance metrics to evaluate model impact and identify high-leverage opportunities for improvement
Collaborate cross-functionally with product, engineering, and data science teams to align technical solutions with business goals and customer experience
Mentor and grow engineering talent, fostering a culture of technical excellence, experimentation and continuous learning
优先资格
Master’s or PhD in relevant technical fields
Experience with search systems, information retrieval or recommendation engines
Experience with LLMs, embeddings, and vector search technologies
Experience with cloud platforms such as AWS, Google Cloud Platform including services like Vertex AI, BigQuery or SageMaker
Experience working 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, Scikit-learn, Keras, XGBoost, LightGMB, and H2o.ai
Experience with ML lifecycle tools such as MLflow, Kubeflow, Weights & Biases, or Amazon SageMaker
Excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders
Demonstrated ability to work independently and manage ambiguity in fast-paced environments
AI 洞察
优缺点分析
优点
- Work on high-impact search and recommendation systems serving millions of users.
- Access to cutting-edge technologies: LLMs, vector search, and transformer models.
- Competitive compensation: base salary $152K-$277K plus bonus and equity.
- Join a fast-growing global e-commerce company with startup culture and resources.
- Dealing with massive scale and real-time constraints requires robust engineering.
- Fast-paced environment may involve tight deadlines and high expectations.
- Need to continuously learn and adapt to evolving ML techniques.
- Experienced ML engineers passionate about search and recommendation, who thrive in big data and real-time systems, and enjoy cross-functional collaboration.
缺点 / 挑战
暂无明显挑战项
角色解读
- Progress to Senior Staff or Principal Engineer, leading larger system architectures.
- Transition into technical leadership roles such as ML Tech Lead or Manager of ML teams.
- Deepen expertise in search, recommendation, or NLP, becoming a domain authority.
- Design and implement large-scale machine learning models for search ranking, ad targeting, and personalized product discovery.
- Build and optimize ML pipelines using Spark, Kubeflow, and MLflow to handle billions of data points.
- Drive innovation in search relevance using LLMs, embeddings, and multi-modal learning.
- Collaborate with cross-functional teams to align technical solutions with business goals.
- Strong proficiency in Python or Java and experience building production-grade ML systems.
- Deep understanding of machine learning, deep learning, and statistical modeling.
- Hands-on experience with modern ML frameworks (TensorFlow, PyTorch) and cloud platforms (AWS, GCP).
- Ability to work with large-scale data systems and serve real-time ML models.
申请策略
- Tailor your resume to highlight impact metrics (e.g., improved CTR, latency reduction).
- Research Coupang's business model and search challenges to show genuine interest.
- Emphasize projects involving large-scale ML systems, especially search or recommendation.
- Showcase proficiency in Python/Java and frameworks like TensorFlow, PyTorch, Spark.
- Highlight experience with cloud platforms (AWS/GCP) and ML lifecycle tools (Kubeflow, MLflow).
- Demonstrate ability to design and serve real-time models in production.
- Strengthen knowledge of LLMs, embeddings, and vector search (e.g., FAISS).
- Gain hands-on experience with Kubeflow or MLflow for pipeline orchestration.
面试指南
- For system design questions: Clarify requirements, propose architecture with trade-offs, discuss scalability and latency, and mention specific tools (e.g., Spark, Kafka, MLflow).
- For behavioral questions: Use STAR (Situation, Task, Action, Result) to highlight your role and measurable outcomes.
- For ML theory: Relate to practical applications at Coupang, such as using transformers for query understanding.
- How would you design a real-time search ranking system for millions of products?
- Describe a time you optimized an ML pipeline for scalability and reproducibility.
- How do you approach feature engineering for a recommendation system?
- Explain the differences between embedding-based retrieval and traditional keyword search.
- How would you evaluate the business impact of a new ranking model?
匹配度报告
75
综合匹配度
High-paying, cutting-edge ML role at a fast-growing e-commerce giant, but on-site with no explicit WLB.
适合人群
This role is best for engineers prioritizing technical growth and compensation over work-life balance.
最强匹配
成长发展匹配
最弱匹配
工作生活匹配
薪资福利85
成长发展90
工作生活50
使命价值75
薪资福利匹配
85较高
The salary range is clearly stated and competitive ($152K-$277K base) with bonus and benefits. This signals strong compensation satisfaction.
薪资信号偏高 (91K-166K/月)
福利待遇Annual bonus is 0-20% of the base salary、Medical/Dental/Vision/Life, AD&D insurance、Flexible Spending Accounts (FSA) & Health Savings Account (HSA)、Long-term/Short-term Disability、Employee Assistance Program (EAP) program、401K Plan with Company Match、18-21 days of the Paid Time Off (PTO) a year、12 Public Holidays、6 weeks Paid Parental leave、Pre-tax commuter benefits、Free Electric Car Charging Station
成长发展匹配
90较高
The role involves state-of-the-art ML techniques (LLMs, transformers, vector search) and provides ample opportunities for skill growth in a fast-paced environment.
技术前沿前沿/新兴技术
技术栈Machine Learning、Deep Learning、Transformer-based models、Contrastive Learning、Vector Search、Large Language Models (LLMs)、Embedding-based retrieval、Multi-modal learning、Apache Spark、Kubeflow、MLflow、TensorFlow、PyTorch
成长机会Mentor and grow engineering talent、fostering a culture of technical excellence, experimentation and continuous learning
业务类型profit_center
工作生活匹配
50较低
The location is Mountain View, on-site only, and no explicit WLB signals. Compensation includes PTO and holidays, but the startup culture may imply long hours.
工作模式仅现场办公
办公地点海外（不适用）
加班情况未提及（无法判断）
使命价值匹配
75中等
Coupang is disrupting e-commerce in South Korea, and improving search/recommendation directly enhances customer experience. The mission 'wow our customers' adds purpose.
行业发展高速增长赛道
社会影响中性/一般
使命信号We exist to wow our customers、Our mission to build the future of commerce is real
创新程度积极采用新技术
Watch Jobs