Senior Recommendation & Growth ML Engineer
RemoteSan Francisco, California, United States
San Francisco, California, United StatesRemoteFull TimeSenior LevelStartup
Full TimeSenior LevelStartup
Job Summary
Design and develop low-latency, real-time recommendation systems across web and mobile platforms, covering trading product discovery, campaign targeting, content ranking, and community feeds. Own the complete machine learning lifecycle from data preparation and feature engineering through offline evaluation, online experimentation, and production deployment. Implement state-of-the-art architectures including two-tower retrieval, sequential models, Graph Neural Networks, and reinforcement learning to drive engagement and user lifetime value. Build predictive models for churn, reactivation, and customer lifetime value while optimizing intervention timing and resource allocation. Develop scalable feature pipelines and experimentation infrastructure supporting causal inference, uplift modeling, and self-service testing. Collaborate with Product, Data Science, and Engineering teams to translate business objectives into scalable solutions serving millions of users globally.
Required Qualifications
- 5+ years of industry experience in Machine Learning Engineering
- 5+ years of industry experience in Recommendation Systems
- 5+ years of industry experience in Personalization Platforms
- 5+ years of industry experience in Growth Engineering
- Experience building and operating recommendation systems at consumer internet scale serving millions of users
- Strong experience with modern recommendation architectures, including Collaborative filtering
- Strong experience with modern recommendation architectures, including Two-tower retrieval models
- Strong experience with modern recommendation architectures, including Sequential recommendation models
- Strong experience with modern recommendation architectures, including Graph Neural Networks (GNN)
- Strong experience with modern recommendation architectures, including Multi-task and multi-objective learning (PLE, MMoE)
- Strong experience with modern recommendation architectures, including Reinforcement learning
- Strong experience with modern recommendation architectures, including Contextual bandits
- Must have experience owning recommendation systems beyond individual model development, including architecture, deployment, experimentation, and business impact measurement
- Strong understanding of Statistical learning
- Strong understanding of Experimentation methodologies
- Strong understanding of Causal inference
- Strong understanding of Treatment effect estimation
- Hands-on experience with Uplift modeling
- Hands-on experience with A/B testing
- Hands-on experience with Conversion optimization
- Hands-on experience with Growth measurement frameworks
- Expert-level Python proficiency
- Strong proficiency in at least one additional language: Java
- Strong proficiency in at least one additional language: Scala
- Strong proficiency in at least one additional language: Go
- Strong proficiency in at least one additional language: C++
- Experience with modern machine learning frameworks: PyTorch
- Experience with modern machine learning frameworks: TensorFlow
- Experience with modern machine learning frameworks: JAX
- Strong experience with large-scale data processing technologies: Spark
- Strong experience with large-scale data processing technologies: Flink
- Strong experience with large-scale data processing technologies: Hive
- Strong experience with large-scale data processing technologies: MapReduce
- Hands-on experience building large-scale ML systems using Kafka
- Hands-on experience building large-scale ML systems using Spark Streaming
- Hands-on experience building large-scale ML systems using Flink
- Hands-on experience building large-scale ML systems using Feature stores (Feast, Tecton, or equivalent)
- Hands-on experience building large-scale ML systems using Online serving infrastructure
- Hands-on experience building large-scale ML systems using Redis
- Hands-on experience building large-scale ML systems using Cassandra
- Experience with Real-time recommendation architectures
- Strong verbal and written communication skills
- Ability to work effectively across Product, Engineering, Data Science, and Leadership teams
- Comfortable operating in a fast-paced, global environment
- Fluent in both English and Mandarin Chinese
- Ability to collaborate directly with engineering and product teams across APAC
- Comfortable serving as a communication bridge between the US R&D Center and Asia-Pacific teams
- Candidates should be comfortable participating in occasional early morning or evening meetings to support cross-time-zone collaboration
Desired Qualifications
- Experience in Cryptocurrency
- Experience in Web3
- Experience in FinTech
- Experience in Trading platforms
- Experience applying LLMs to recommendation and personalization systems
- Experience building hybrid recommendation and AI agent architectures
- Experience with Multi-scenario joint modeling
- Experience with User lifetime value prediction
- Experience with Budget optimization
- Experience with Operations research
- Experience building recommendation systems for social or community platforms, including Feed ranking
- Experience building recommendation systems for social or community platforms, including Content personalization
- Experience building recommendation systems for social or community platforms, including Creator-user matching
- Publications in leading AI and machine learning conferences such as KDD
- Publications in leading AI and machine learning conferences such as NeurIPS
- Publications in leading AI and machine learning conferences such as ICML
- Publications in leading AI and machine learning conferences such as ICLR
- Publications in leading AI and machine learning conferences such as WWW
- Publications in leading AI and machine learning conferences such as SIGIR
- Publications in leading AI and machine learning conferences such as WSDM
- Publications in leading AI and machine learning conferences such as CIKM
- Experience helping establish engineering organizations, new product lines, or early-stage R&D teams
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