Senior Recommendation & Growth ML Engineer

Headcount: 2
Level: P6
Location: US R&D Center (Global collaboration across APAC)

About the Role

We are looking for a Senior Recommendation & Growth ML Engineer to build and scale the machine learning systems that power personalization, user growth, and intelligent product discovery across Bybit’s ecosystem.

This role sits at the intersection of recommendation systems, growth engineering, experimentation, and AI-driven personalization. You will own the end-to-end development of large-scale recommendation and growth optimization systems that drive engagement, retention, conversion, and user lifetime value across trading products, campaigns, community experiences, and AI-powered investment services.

You will work closely with Product, Data Science, TradeGPT, Community, and Engineering teams to translate business objectives into scalable machine learning systems serving millions of users globally.

This is a highly impactful role for someone who combines deep recommendation system expertise with strong engineering execution and a passion for applying cutting-edge AI technologies to real-world products.

What You'll Do

Build Personalized Recommendation Systems

Design and develop low-latency, real-time recommendation systems across web and mobile platforms.
Drive personalization for:
- Trading product discovery
- Campaign targeting
- Content recommendation
- Community feed ranking (ByX)
- User engagement experiences
Own the complete machine learning lifecycle, including:
- Data preparation
- Feature engineering
- Model development
- Offline evaluation
- Online experimentation
- Production deployment

Advance Recommendation & Personalization Algorithms

Design and implement state-of-the-art recommendation models, including:
- Two-tower retrieval architectures
- Sequential recommendation models
- Graph Neural Networks (GNN)
- Multi-task and multi-objective learning models (PLE, MMoE)
- Reinforcement learning and contextual bandits
Explore multi-scenario joint modeling to leverage signals across:
- Trading products
- Community platforms
- Marketing campaigns
- User growth funnels

Power AI-Driven Investment Experiences

Build the personalization layer behind TradeGPT’s AI investment assistant.
Integrate:
- Portfolio signals
- Trading behavior
- Market intelligence
- On-chain data
- User preferences
Deliver:
- Personalized token recommendations
- Intelligent market alerts
- AI-driven investment insights
- Agent-powered investment workflows
Research and implement hybrid recommendation architectures that combine:
- Traditional recommendation models
- Large Language Models (LLMs)
- Agent-based AI systems

Drive User Growth Through Machine Learning

Develop predictive models for:
- User churn
- Reactivation likelihood
- Upgrade propensity
- Customer lifetime value (LTV)
Apply causal inference and optimization techniques to improve growth outcomes, including:
- Uplift modeling
- Difference-in-Differences (DiD)
- Treatment effect estimation
- Resource allocation optimization
Optimize intervention timing, communication channels, and incentive strategies to maximize conversion efficiency.

Build User Lifecycle Intelligence

Develop a comprehensive user value and lifecycle framework.
Identify behavioral signals that predict key milestones such as:
- First deposit
- First trade
- Product adoption
- VIP upgrades
Design personalized engagement strategies that connect users with the most relevant products and experiences, including:
- Copy Trading
- TradeGPT
- Earn products
- Campaigns and rewards
Measure and validate business impact through rigorous experimentation.

Own Experimentation & Measurement Systems

Design and enhance experimentation infrastructure supporting:
- Experiment assignment
- Metrics pipelines
- Statistical analysis
- Self-service experimentation
Implement advanced experimentation methodologies including:
- CUPED
- Sequential testing
- Variance reduction techniques
Lead conversion lift studies and impact measurement initiatives to quantify recommendation system performance.

Build Scalable ML Infrastructure

Develop and maintain real-time and batch feature pipelines supporting recommendation and growth models.
Partner with Data Engineering teams on:
- Feature store architecture
- Data quality frameworks
- Real-time data pipelines
Ensure production systems are highly observable, debuggable, and reliable at scale.

Technical Leadership

Collaborate closely with Product, Data Science, Community, TradeGPT, and Engineering teams.
Translate business goals into scalable machine learning solutions.
Establish engineering standards and best practices.
Conduct design reviews and mentor junior engineers.
Help scale the technical capabilities of the US R&D organization.

Required Qualifications

Experience

5+ years of industry experience in:
- Machine Learning Engineering
- Recommendation Systems
- Personalization Platforms
- Growth Engineering
Experience building and operating recommendation systems at consumer internet scale serving millions of users.

Recommendation Systems Expertise

Strong experience with modern recommendation architectures, including:

Collaborative filtering
Two-tower retrieval models
Sequential recommendation models
Graph Neural Networks (GNN)
Multi-task and multi-objective learning (PLE, MMoE)
Reinforcement learning
Contextual bandits

Must have experience owning recommendation systems beyond individual model development, including architecture, deployment, experimentation, and business impact measurement.

Machine Learning & Statistics

Strong understanding of:
- Statistical learning
- Experimentation methodologies
- Causal inference
- Treatment effect estimation
Hands-on experience with:
- Uplift modeling
- A/B testing
- Conversion optimization
- Growth measurement frameworks

Programming & Data Engineering

Expert-level Python proficiency.
Strong proficiency in at least one additional language:
- Java
- Scala
- Go
- C++
Experience with modern machine learning frameworks:
- PyTorch
- TensorFlow
- JAX
Strong experience with large-scale data processing technologies:
- Spark
- Flink
- Hive
- MapReduce

Distributed Systems & Infrastructure

Hands-on experience building large-scale ML systems using:

Kafka
Spark Streaming
Flink
Feature stores (Feast, Tecton, or equivalent)
Online serving infrastructure
Redis
Cassandra
Real-time recommendation architectures

Communication & Collaboration

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.

Language Requirements

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.

Working Hours Flexibility

This role requires regular collaboration with teams across:
- Singapore
- Dubai
- Other Asia-Pacific locations
Candidates should be comfortable participating in occasional early morning or evening meetings to support cross-time-zone collaboration.

Preferred Qualifications

Experience in:
- Cryptocurrency
- Web3
- FinTech
- Trading platforms
Experience applying LLMs to recommendation and personalization systems.
Experience building hybrid recommendation and AI agent architectures.
Experience with:
- Multi-scenario joint modeling
- User lifetime value prediction
- Budget optimization
- Operations research
Experience building recommendation systems for social or community platforms, including:
- Feed ranking
- Content personalization
- Creator-user matching
Publications in leading AI and machine learning conferences such as:
- KDD
- NeurIPS
- ICML
- ICLR
- WWW
- SIGIR
- WSDM
- CIKM
Experience helping establish engineering organizations, new product lines, or early-stage R&D teams.

Ideal Candidate Profile

Full-Stack Recommendation Leader

Deep expertise across the entire recommendation lifecycle—from retrieval and ranking to experimentation and production systems.
Proven track record delivering recommendation platforms at large consumer scale.
Strong engineering mindset with the ability to architect systems, not just build models.

AI & Agent-Oriented Innovator

Understands the convergence of recommendation systems and generative AI.
Has practical experience building AI-assisted products, intelligent agents, or LLM-powered personalization systems.
Able to integrate recommendation capabilities into next-generation AI experiences.

Infrastructure & Technical Leadership

Experienced in designing foundational recommendation and machine learning platforms.
Brings strong engineering rigor, architecture standards, and operational excellence.
Capable of mentoring engineers and helping scale a high-performing machine learning organization.

Why Join Us?

Shape Personalization for a Global Platform

Build recommendation and growth systems that influence how millions of users discover products, engage with content, and interact with AI-powered financial experiences.

Work at the Frontier of AI and Recommendation Systems

Help define how traditional recommendation technologies, large language models, and autonomous agents converge to create the next generation of personalized user experiences.

End-to-End Ownership

Own everything from model design and experimentation to deployment and business impact measurement, with direct visibility into product outcomes and company growth.

High-Impact, High-Velocity Environment

Join a fast-moving team where machine learning innovation quickly translates into production systems and measurable user impact at global scale.