Principal Machine Learning Engineer, Recommendation Platform (Feature Platform)
ByLabs · Seattle, Washington, United States
FULL TIMEpermanent
Job Description
About the RoleAs a Principal Recommendation Platform Engineer, you will help upgrade our core recommendation architecture and feature platform, evolving our current rule-based personalization system into an industrial-grade, large-scale, real-time machine learning recommendation platform. The role spans key business scenarios, including trading products, content feeds, the community, and AI-powered investment workflows such as TradeGPT.You will work closely with recommendation algorithm, growth product and operations, and data science teams to build a high-throughput, low-latency, highly available foundation for personalization at scale, serving more than 80 million users globally. You will lead architectural decisions, infrastructure development, and end-to-end delivery, taking the platform from zero to one in a resource-constrained, fast-moving environment.
Have you got what it takes to succeed The following information should be read carefully by all candidates.
Key ResponsibilitiesDevelop high-throughput, low-latency recommendation services. Continuously improve system performance and reliability, and establish robust degradation and disaster-recovery mechanisms.Build stream-and-batch feature computation and training data pipelines to support minute-level updates of user behavior features. Develop a Feature Store that unifies online and offline features, address temporal feature leakage and feature consistency issues, and support faster model iteration and performance improvements.Identify common architectural needs across recommendation scenarios—including trading products, content feeds, and community—and optimize their workflows. Build reusable platform capabilities to address technical challenges and business requirements.Develop and optimize core recommendation components, and drive the engineering and business adoption of emerging technologies, including LLMs and AI agents.
Major RequirementsAt least 4 years of recommender systems engineering experience at a consumer internet company. You have played a significant role in, or led, the launch or architectural modernization of a real-time recommendation system serving tens of millions of users. Experience building a recommendation system or feature platform from the ground up is preferred.Strong computer science fundamentals and proficiency in at least one of Go, Java, or C++. You write well-structured code, apply sound design principles, and enjoy tackling challenging engineering problems.Experience with stream-and-batch data processing and feature and training data pipelines, including hands-on experience resolving stream-processing latency and data backlog issues. Familiarity with the deployment and performance tuning of mainstream big-data systems, key-value stores, and vector databases.A platform-oriented approach to architecture and standardization. Experience developing core modules for a large-scale recommendation platform, feature platform, experimentation platform, or high-performance RPC framework is preferred.A deep understanding of the recommendation system lifecycle, with the ability to collaborate effectively with algorithm teams and translate model requirements into reliable production systems.
Nice-to-have:Architecture experience in search, advertising, or recommendations at major technology companiesExperience as a key engineer building cross-business feature platforms, large-scale online and offline feature services, or embedding feature pipelines.Experience with multi-region deployment, data synchronization, active-active architectures, or cross-region RPC latency optimization.An AI-driven approach and hands-on experience bringing LLM or agent applications into production for search, advertising, or recommendation use cases.
Why Join UsA multi-domain recommendation challenge in fintech: Go beyond a single domain such as video or e-commerce. You will build personalization capabilities across trading products, market intelligence, community content, campaigns, and AI-powered investment workflows.Proven business impact with substantial room for ML advancement: Our current recommendation system has demonstrated a 71% increase in CTR and a 1,285% increase in conversion rate, with significant potential for an ML-native upgrade.Work at the intersection of AI and recommendations: Build where traditional recommender systems, generative AI, and LLM agents meet—one of the most active frontiers in engineering.An opportunity to lead, not just follow: Our combination of trading, content, community, AI, and campaigns creates distinctive technical challenges and enables rapid iteration. xzbcwsv Global scale: Serve more than 80 million users across over 200 countries, tackling the real-world complexity of large-scale, real-time systems.
Have you got what it takes to succeed The following information should be read carefully by all candidates.
Key ResponsibilitiesDevelop high-throughput, low-latency recommendation services. Continuously improve system performance and reliability, and establish robust degradation and disaster-recovery mechanisms.Build stream-and-batch feature computation and training data pipelines to support minute-level updates of user behavior features. Develop a Feature Store that unifies online and offline features, address temporal feature leakage and feature consistency issues, and support faster model iteration and performance improvements.Identify common architectural needs across recommendation scenarios—including trading products, content feeds, and community—and optimize their workflows. Build reusable platform capabilities to address technical challenges and business requirements.Develop and optimize core recommendation components, and drive the engineering and business adoption of emerging technologies, including LLMs and AI agents.
Major RequirementsAt least 4 years of recommender systems engineering experience at a consumer internet company. You have played a significant role in, or led, the launch or architectural modernization of a real-time recommendation system serving tens of millions of users. Experience building a recommendation system or feature platform from the ground up is preferred.Strong computer science fundamentals and proficiency in at least one of Go, Java, or C++. You write well-structured code, apply sound design principles, and enjoy tackling challenging engineering problems.Experience with stream-and-batch data processing and feature and training data pipelines, including hands-on experience resolving stream-processing latency and data backlog issues. Familiarity with the deployment and performance tuning of mainstream big-data systems, key-value stores, and vector databases.A platform-oriented approach to architecture and standardization. Experience developing core modules for a large-scale recommendation platform, feature platform, experimentation platform, or high-performance RPC framework is preferred.A deep understanding of the recommendation system lifecycle, with the ability to collaborate effectively with algorithm teams and translate model requirements into reliable production systems.
Nice-to-have:Architecture experience in search, advertising, or recommendations at major technology companiesExperience as a key engineer building cross-business feature platforms, large-scale online and offline feature services, or embedding feature pipelines.Experience with multi-region deployment, data synchronization, active-active architectures, or cross-region RPC latency optimization.An AI-driven approach and hands-on experience bringing LLM or agent applications into production for search, advertising, or recommendation use cases.
Why Join UsA multi-domain recommendation challenge in fintech: Go beyond a single domain such as video or e-commerce. You will build personalization capabilities across trading products, market intelligence, community content, campaigns, and AI-powered investment workflows.Proven business impact with substantial room for ML advancement: Our current recommendation system has demonstrated a 71% increase in CTR and a 1,285% increase in conversion rate, with significant potential for an ML-native upgrade.Work at the intersection of AI and recommendations: Build where traditional recommender systems, generative AI, and LLM agents meet—one of the most active frontiers in engineering.An opportunity to lead, not just follow: Our combination of trading, content, community, AI, and campaigns creates distinctive technical challenges and enables rapid iteration. xzbcwsv Global scale: Serve more than 80 million users across over 200 countries, tackling the real-world complexity of large-scale, real-time systems.
Details
| Company | ByLabs |
| Location | Seattle, Washington, United States |
| Type | FULL TIME |
| Niche | finance |
| Experience | permanent |