Tencent Cloud Principal Engineer, Data+AI Infrastructure (Big Data Platform, Singapore)

About the Role

Tencent Cloud's Big Data team is hiring a Principal Engineer to help define data infrastructure for the AI era. We build data systems that power AI, and we are also bringing AI into the core of storage, compute, and operations, so that our infrastructure can optimize itself and run reliably with less manual effort.

Our work covers two areas. Infra for AI is about building data infrastructure that serves AI workloads. AI for Infra is about using AI to make that infrastructure smarter. At production scale, you will work on hard problems in data throughput, CPU/GPU utilization, and system reliability. You will help set technical direction and architecture, and take ideas from problem definition and research through prototyping to production. We value work that ships, and we also encourage open-source contributions and published research.

We welcome candidates with deep expertise in one or more of the following areas.

What You'll Do

1. Compute: Build distributed compute engines for data and AI workloads. Work on query and operator optimization, CPU/GPU scheduling, workload forecasting, and autoscaling. Help these systems move from static rules to adaptive tuning, and deliver better performance per unit of compute.

2. Storage: Remove data bottlenecks for AI. Design storage architectures for large-scale, multimodal, fast-changing data, covering tiering, hot/cold data detection, data layout, compression, caching, and I/O optimization. Deliver a fast, reliable, and cost-efficient data layer for model training, inference, and AI agents.

3. Data & Search: Combine LLMs, agents, and data systems to create new ways of working with data, such as natural-language queries, text-to-SQL, and automated analytics. Build multimodal data processing, data quality, and metadata capabilities that make enterprise data trustworthy, traceable, and ready for AI. Develop search capabilities, including query understanding, hybrid retrieval, embeddings and reranking, agentic search, and RAG.

4. Self-Managing Systems: Build closed-loop systems for monitoring, anomaly detection, root-cause analysis, and automated remediation. Solve hard problems in diagnostics, performance tuning, and capacity planning. Use safeguards such as rigorous evaluation, access controls, canary releases, and rollback so that automation stays safe.

5. Platform Architecture: Help design the next-generation cloud data platform for AI-native workloads, bringing storage, compute, retrieval, and model serving together. Own key modules or cross-module architecture, and make sound trade-offs across performance, reliability, openness, and cost.

6. Research to Production: Identify high-impact open problems across data infrastructure, AI infrastructure, and AI for infra. Apply ML, LLMs, agents, and reinforcement learning to real system problems, including RL reward modeling, policy optimization, and feedback learning. Train, fine-tune, and distill in-house models for data understanding, retrieval ranking, and system optimization. Take work from hypothesis to benchmarking to production, and share results through open source and top-tier conferences.

What We're Looking For

1. Bachelor's degree or higher in Computer Science or a related field, with 5+ years of software engineering experience. You have strong fundamentals in data structures, algorithms, operating systems, and networking, and strong system design skills.

2. Excellent written and spoken English is required. Proficiency in Chinese Mandarin is required as approximately 50% of the role involves collaborating with China headquarters on business discussions, meetings, and documentation

3. Expert in at least one of C++, Python, Java, Go, or Rust. You write high-quality, well-tested code and can debug complex systems.

4. Hands-on experience building storage, compute, database, data infrastructure, or AI infrastructure systems, with deep knowledge of at least one of these areas. Experience with distributed storage, query engines, database internals, data lakehouses, or resource scheduling is preferred.

5. Strong performance engineering skills. You can find bottlenecks across the full stack, from algorithms and models to compute, storage, networking, and scheduling, and you can prove improvements in performance, stability, and cost through profiling, benchmarking, and metrics.

6. Solid understanding of machine learning, LLMs, or AI agents. You can judge when AI is the right solution for a system problem, weighing accuracy, cost, and reliability, and you can take it from prototype to production.

7. A track record of leading architecture for large-scale core systems. You can set a long-term technical roadmap and phased delivery plan, run cross-team design reviews, lead complex technical work, and mentor engineers.

8. Strong ownership, independent problem-solving, and cross-team collaboration. You can break down ambiguous problems into clear, testable solutions and drive them into production.

Preferred Qualifications

1. Top-tier publications and research: Papers published or accepted at top conferences in databases, storage, systems, or AI, such as SIGMOD, VLDB, ICDE, FAST, SOSP, OSDI, NSDI, NeurIPS, ICML, ICLR, or MLSys, with a clear account of your own contribution. Experience reproducing research, releasing open-source implementations, or turning research into engineering results is a plus.

2. AI for Infra experience: Hands-on projects that applied ML, LLMs, Agents, or reinforcement learning to query optimization, resource scheduling, storage optimization, performance tuning, AIOps, or data management, with measurable results.

3. Open-source and large-scale engineering: Contributions to relevant open-source projects, or leadership of key module development in ecosystems such as databases, Spark, or Ray. Production experience with GPU operators, inference engines, multimodal data pipelines, cloud-native multi-tenant systems, or serverless systems.