job description
Join ByteDance, the global technology powerhouse behind TikTok, Douyin, and other innovative platforms, as a Machine Learning System Engineer in our elite Soaring Star Talent Program. This is your chance to work at the forefront of AI-driven systems in one of the world’s most dynamic tech environments—now with the flexibility to work from Bali’s vibrant tech hubs (Canggu, Ubud, Denpasar, or Jimbaran).
The Data AML (Anti-Money Laundering) team is ByteDance’s machine learning middle platform, powering cutting-edge training and inference systems for recommendation engines, fraud detection, and large-scale data processing. As a Machine Learning System Engineer, you’ll design, optimize, and scale the infrastructure that fuels ByteDance’s AI capabilities, impacting millions of users worldwide. This role blends systems engineering, distributed computing, and machine learning to solve complex challenges in performance, reliability, and efficiency.
Bali’s thriving digital nomad community and ByteDance’s hybrid work model offer the perfect balance of innovation, flexibility, and work-life harmony. You’ll collaborate with top-tier engineers globally while enjoying Bali’s stunning landscapes, affordable living, and a supportive expat ecosystem. Whether you’re optimizing GPU clusters, debugging distributed training pipelines, or deploying real-time inference systems, your work will shape the future of AI at scale.
This is more than a job—it’s a career accelerator. The Soaring Star Talent Program provides mentorship from industry leaders, rapid growth opportunities, and exposure to ByteDance’s global projects. If you’re passionate about ML systems, distributed computing, or high-performance infrastructure, we want you on our team.
Responsibility
- Design, develop, and optimize large-scale machine learning training and inference systems for ByteDance’s Data AML platform, ensuring high performance, scalability, and reliability.
- Collaborate with ML researchers and product teams to translate algorithmic innovations into production-ready systems, bridging the gap between theory and real-world impact.
- Build and maintain distributed computing frameworks (e.g., TensorFlow, PyTorch, Ray) to support training and serving of recommendation models, fraud detection systems, and other AI applications.
- Debug and resolve performance bottlenecks in GPU/CPU clusters, network latency, or storage systems to maximize efficiency and reduce costs.
- Develop automated pipelines for model deployment, monitoring, and A/B testing, ensuring seamless integration with ByteDance’s global infrastructure.
- Optimize resource allocation (e.g., GPU scheduling, memory management) for machine learning workloads to balance cost and performance.
- Contribute to open-source projects and publish technical blogs or papers to share innovations with the broader ML community.
- Mentor junior engineers and participate in code reviews, architecture discussions, and cross-team collaborations to uphold engineering excellence.
Qualifications
- Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Mathematics, or a related field (or equivalent practical experience).
- 3+ years of experience in machine learning systems, distributed computing, or backend engineering, with a focus on scalability and performance optimization.
- Proficiency in Python, C++, or Go, with hands-on experience in TensorFlow, PyTorch, or similar ML frameworks.
- Strong understanding of distributed systems (e.g., Kubernetes, Docker, Spark, Flink) and cloud platforms (AWS, GCP, or ByteDance’s internal cloud).
- Experience with GPU computing, CUDA, or hardware acceleration for machine learning workloads.
- Familiarity with data processing pipelines (e.g., Kafka, Flink, Hadoop) and real-time inference systems.
- Solid grasp of algorithms, data structures, and system design principles, with the ability to debug complex performance issues.
- Excellent problem-solving skills and a collaborative mindset—you thrive in fast-paced, cross-functional teams.
- Bonus: Experience with recommendation systems, fraud detection, or large-scale data processing.