Backend Engineer , AML Engine Orchestration

ByteDance; Singapore; On-site; SGD 70,000 - 100,000; Full time

Job summary

A leading tech company in Singapore is seeking an experienced professional to develop next-generation machine learning algorithms and distributed systems. Responsibilities include optimizing resource efficiency and building training architectures for recommendation models. The ideal candidate has a bachelor's degree in Computer Science and at least 3 years of experience in large-scale distributed systems, with strong programming skills in Golang or Python.

Benefits

Positive team atmosphere

Career growth opportunity

Paid leave

Flat organization

Access to a large user base

Qualifications

• Minimum 3 years of relevant experience in large-scale distributed systems.
• Strong programming skills with modern languages like Golang and Python.
• Experience in designing or contributing to multi-tenant systems.

Responsibilities

• Develop frameworks for resource efficiency optimization.
• Build a training architecture for ultra-large recommendation models.
• Design orchestration architecture for online recommendation systems.

Skills

Programming experience in Golang

Coding experience in Python

Analytical abilities

Problem-solving skills

Strong communication

Education

Bachelor's degree in Computer Science or related fields

Tools

Kubernetes

Yarn

Flink

Spark

Job description

About Us

Founded in 2012, ByteDance's mission is to inspire creativity and enrich life. With a suite of more than a dozen products, including TikTok, Lemon8, CapCut and Pico as well as platforms specific to the China market, including Toutiao, Douyin, and Xigua, ByteDance has made it easier and more fun for people to connect with, consume, and create content.

Why Join ByteDance

Inspiring creativity is at the core of ByteDance's mission. Our innovative products are built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and enrich life - a mission we work towards every day.

As ByteDancers, we strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our Company, and our users. When we create and grow together, the possibilities are limitless. Join us.

Diversity & Inclusion

ByteDance is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At ByteDance, our mission is to inspire creativity and enrich life. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.

Job highlights

Positive team atmosphere, Career growth opportunity, Paid leave, Flat organization, 100+ mil users

Responsibilities

ByteDance will be prioritizing applicants who have a current right to work in Singapore, and do not require ByteDance's sponsorship of a visa.

Team Introduction

The mission of our AML team is to push next-generation machine learning algorithms and platforms for the recommendation system, ads ranking and search ranking in our company. We also drive substantial impact on core businesses of the company.

Responsibilities:

1. Resource Efficiency Optimization in Distributed Orchestration and Scheduling:
   • Develop and extend distributed orchestration frameworks within the Kubernetes/Godel ecosystem. Select appropriate frameworks based on different business scenarios, and optimize cluster utilization and load balancing strategies according to the specific characteristics of each scenario;
   • Integrate and expand AutoScaling and automatic parallelization capabilities for various models and tasks. Employ load modeling and analytic methods for different models to automatically optimize resource requests, achieving large-scale improvements in resource usage efficiency and global optimality;
   • Responsible for preemption and re-scheduling mechanisms for services with different prioritties, and manage automatic resource multiplexing across different clusters and resource types; handle scheduling and load adaptation across multi-datacenter, multi-region, and multi-cloud environments.
2. Building Training System Architecture for Next-Generation Ultra-Large and Ultra-Deep Recommendation Models:
   • Develop a flexible, elastic and robust distributed training runtime focused on hyper-scaled embeddings and large-scale GPU training;
   • Design and optimize distributed computing APIs and runtimes geared towards future recommendation and ads model paradigms (e.g., reinforcement learning, fine-tuning and/or distillation);
   • Collaborate with platform teams to enhance the diagnosability and usability of distributed training systems.
3. Constructing Online Orchestration Architecture for Next-Generation Recommendation Systems:
   • Build a robust and stable distributed model inference architecture for online learning scenarios involving hyper-scaled embeddings;
   • Optimize the usability of online recommendation and ads model architectures and MLops workflows.

Qualifications

Minimum Qualifications

• Bachelor's degree or above, majoring in Computer Science, Engineering or related fields.
• Strong programming and coding experience with at least one modern language such as Golang, Python.
• Experience contributing to the large scale distributed systems, multi-tenant systems (architecture, reliability and scaling).
• Strong analytical abilities and problem solving.
• Good communication, self-motivation, engineering practice, documentation, etc.
• At least 3 years of relevant experience.

Preferred Qualifications

• Familiar with large-scale distributed scheduling systems like Kubernetes, Yarn, Flink and/or Spark
• Familiar with opensourced orchestration frameworks like VeRL, vLLM, Ray or TFX, etc.