Senior Backend Software Engineer (AI Infrastructure / Artifact Management) - Developer Services

Team Introduction The Developer Services team builds and evolves the core R&D infrastructure that powers software delivery at scale across ByteDance, spanning build systems, artifact management, artifact distribution, and the software supply chain. Our platforms support a broad range of businesses, technology stacks, and delivery scenarios. Improvements to architecture, performance, or developer experience can be adopted quickly at scale and translated into tangible business impact.

Building on this foundation, we are expanding into AI-native infrastructure, including infrastructure for large-scale AI training and inference, artifact context, lifecycle management for Skills as first-class artifacts, and a widely used Skill Market in ByteDance. We also continuously explore and apply technologies such as Agent Harness, closed-loop execution, tool use, context engineering, evaluation, and observability in real-world scenarios. This creates a unique opportunity to combine the rigor and scale of core infrastructure with cutting-edge AI engineering.

Responsibilities

1. Design, develop, and continuously evolve the company's R&D infrastructure across build systems, artifact management, data distribution, and software supply chain. Deliver stable, efficient, and easy-to-use platform capabilities that support multiple programming languages, hardware architectures, and global software delivery;
2. Build infrastructure for large-scale AI training and inference. Drive performance and efficiency improvements across container image build, image and artifact distribution, caching, storage, and cross-region data transfer to address the challenges of high concurrency, massive throughput, and rapid scaling;
3. Build and evolve a company-wide ecosystem for AI extensibility and the Skill Market, serving both the company-wide public marketplace and private team workspaces. Support the development, versioning, publishing, distribution, quality evaluation, and governance of Skills, Plugins, and other extension formats, making AI capabilities easier to create and adopt;
4. Explore and apply AI technologies to build domain-specific Agents. Turn domain knowledge, business data, and system tools into capabilities that can be understood, invoked, and executed by Agents. Address challenges in build troubleshooting, on-call operations, SDK dependency upgrades, risk governance, fault diagnosis, and automated remediation, advancing AI from assisted analysis toward closed-loop resolution;
5. Architect and optimize large-scale distributed systems to continuously improve throughput, reliability, observability, and developer experience. Work closely with engineering, security, infrastructure, and AI business teams to understand real-world requirements and drive complex projects from design to adoption at scale.Minimum Qualifications
6. Bachelor's degree or higher in Computer Science or a related field, with experience in designing, building, and continuously evolving large-scale backend systems;
7. Proficiency in one or more programming languages, such as Go, Java, C++, Python, or Rust, with strong computer science fundamentals, sound coding practices, and solid engineering skills;
8. Familiarity with distributed systems and hands-on experience in one or more areas such as storage, computing, task scheduling, caching, messaging, observability, or reliability engineering;
9. Strong problem-solving, system design, and execution skills, with the ability to diagnose issues across complex system paths and drive long-term, systematic solutions;
10. Curiosity and enthusiasm for technology, an interest in applying AI to software engineering, strong learning and ownership, and the ability to communicate, collaborate, and drive projects effectively.

Preferred Qualifications

1. Experience in DevOps, CI/CD, build systems, artifact repositories, cloud-native technologies, or software supply chain security;
2. Hands-on experience with AI Agents, RAG, tool use, MCP/Skills, AI evaluation, or AI observability;
3. Experience with large-scale distributed systems, globally distributed multi-region systems, or infrastructure for AI training and inference, or contributions to relevant open-source communities.