美团(meituan)招聘Principal Software Engineer
招聘职位：
Principal Software Engineer 搜索同类职位
部门介绍：
美团核心本地商业下业务研发平台，基于公司“零售+科技”的战略导向，通过从底层架构到应用系统、从软件到硬件、从传统算法到GenAI的全方位技术研究，以及开发、运维、安全等全流程能力建设，致力于推动美团核心本地商业下需求侧与供给侧的数字化和智能化升级，助力业务实现高质量增长，优化用户使用体验，提升商户经营水平，促进行业生态伙伴关系的持续健康发展。团队秉持务实、自驱、开放以及追求卓越的工程师文化，一方面通过系统和技术体系的持续迭代升级，帮助业务高质量增长；另一方面，密切关注前沿技术趋势变化，积极创新技术能力，为业务未来发展创造新的可能性。期待优秀的你加入我们，在夯实专业、深耕平台的基础上与业务携手同行，一起用技术 “帮大家吃得更好，生活更好”。
岗位职责：
Technical Leadership & Architecture
• Define and drive the technical vision and long-term architecture strategy for AI/MLplatforms, model training/serving infrastructure, and large-scale data systems
• Lead the design of next-generation AI infrastructure including model training pipelines,feature stores, real-time inference systems, and GPU/accelerator cluster management
• Make high-impact design decisions on distributed systems that handle millions of QPSand petabyte-scale data processing
• Establish and champion engineering best practices for AI system reliability, performanceoptimization, and cost efficiency
AI/ML Systems & Infrastructure
• Architect and build scalable ML platforms that accelerate model development, training,evaluation, and deployment lifecycles
• Design high-performance inference serving systems with low-latency requirements forreal-time AI applications
• Drive innovation in LLM (Large Language Model) infrastructure, including fine-tuning,RLHF pipelines, and efficient serving frameworks
• Lead the development of foundational infrastructure components: container orchestration,service mesh, observability, and CI/CD for ML workloads
• Optimize compute resource utilization across heterogeneous hardware (GPU, TPU,custom accelerators)
Execution & Delivery
• Hands-on contribution to the most complex and ambiguous technical challenges in AI andinfrastructure
• Own end-to-end delivery of large-scale, cross-team projects from concept to production
• Drive technical feasibility assessments for new AI product initiatives and infrastructureinvestments
• Identify and resolve systemic technical risks, performance bottlenecks, and reliabilityconcerns
Influence & Mentorship
• Partner with senior leadership (VP/Director level) to align AI/infrastructure strategy withbusiness objectives
• Drive cross-functional collaboration across AI research, engineering, product, andplatform teams
• Mentor and coach senior engineers, fostering a culture of technical excellence andinnovation
• Raise the engineering bar through design reviews, code reviews, and architecturalguidance
• Influence the broader tech community through open-source contributions, publications, orconference talks
任职要求：
• 5+ years of professional software engineering experience, with a strong track record inAI/ML systems or large-scale infrastructure
• Deep expertise in distributed systems architecture: microservices, event-drivenarchitecture, data-intensive applications at massive scale
• Proven experience building and operating AI/ML platforms or infrastructure (modeltraining, serving, feature engineering, or MLOps)
• Strong knowledge of deep learning frameworks (PyTorch, TensorFlow, JAX) and MLsystem internals
• Proficiency in 2+ programming languages (e.g., Python, Go, C++, Rust, Java, Scala)
• Extensive experience with cloud-native technologies at scale: Kubernetes, Docker,AWS/GCP/Azure, CI/CD pipelines
• Familiarity with GPU programming and high-performance computing concepts (CUDA,distributed training, model parallelism)
• Demonstrated ability to drive cross-team/cross-org technical alignment and influencewithout authority
• Excellent communication skills — ability to articulate complex technical concepts todiverse audiences
• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
岗位亮点：
???? Global Reach
Open to global tech talent, not geo-restricted
Explicitly requires experience in globally distributed teams across time zones
Remote/hybrid options — very appealing to international candidates
???? AI + Infrastructure Dual Focus
Rare intersection role: not pure AI research, not pure infra — the bridge between both
Covers the hottest areas: LLM infrastructure (training, fine-tuning, RLHF, efficient serving)
GPU cluster management & heterogeneous compute (A100/H100, CUDA, NCCL) — seriously hard-core
美团核心本地商业下业务研发平台，基于公司“零售+科技”的战略导向，通过从底层架构到应用系统、从软件到硬件、从传统算法到GenAI的全方位技术研究，以及开发、运维、安全等全流程能力建设，致力于推动美团核心本地商业下需求侧与供给侧的数字化和智能化升级，助力业务实现高质量增长，优化用户使用体验，提升商户经营水平，促进行业生态伙伴关系的持续健康发展。团队秉持务实、自驱、开放以及追求卓越的工程师文化，一方面通过系统和技术体系的持续迭代升级，帮助业务高质量增长；另一方面，密切关注前沿技术趋势变化，积极创新技术能力，为业务未来发展创造新的可能性。期待优秀的你加入我们，在夯实专业、深耕平台的基础上与业务携手同行，一起用技术 “帮大家吃得更好，生活更好”。
岗位职责：
Technical Leadership & Architecture
• Define and drive the technical vision and long-term architecture strategy for AI/MLplatforms, model training/serving infrastructure, and large-scale data systems
• Lead the design of next-generation AI infrastructure including model training pipelines,feature stores, real-time inference systems, and GPU/accelerator cluster management
• Make high-impact design decisions on distributed systems that handle millions of QPSand petabyte-scale data processing
• Establish and champion engineering best practices for AI system reliability, performanceoptimization, and cost efficiency
AI/ML Systems & Infrastructure
• Architect and build scalable ML platforms that accelerate model development, training,evaluation, and deployment lifecycles
• Design high-performance inference serving systems with low-latency requirements forreal-time AI applications
• Drive innovation in LLM (Large Language Model) infrastructure, including fine-tuning,RLHF pipelines, and efficient serving frameworks
• Lead the development of foundational infrastructure components: container orchestration,service mesh, observability, and CI/CD for ML workloads
• Optimize compute resource utilization across heterogeneous hardware (GPU, TPU,custom accelerators)
Execution & Delivery
• Hands-on contribution to the most complex and ambiguous technical challenges in AI andinfrastructure
• Own end-to-end delivery of large-scale, cross-team projects from concept to production
• Drive technical feasibility assessments for new AI product initiatives and infrastructureinvestments
• Identify and resolve systemic technical risks, performance bottlenecks, and reliabilityconcerns
Influence & Mentorship
• Partner with senior leadership (VP/Director level) to align AI/infrastructure strategy withbusiness objectives
• Drive cross-functional collaboration across AI research, engineering, product, andplatform teams
• Mentor and coach senior engineers, fostering a culture of technical excellence andinnovation
• Raise the engineering bar through design reviews, code reviews, and architecturalguidance
• Influence the broader tech community through open-source contributions, publications, orconference talks
任职要求：
• 5+ years of professional software engineering experience, with a strong track record inAI/ML systems or large-scale infrastructure
• Deep expertise in distributed systems architecture: microservices, event-drivenarchitecture, data-intensive applications at massive scale
• Proven experience building and operating AI/ML platforms or infrastructure (modeltraining, serving, feature engineering, or MLOps)
• Strong knowledge of deep learning frameworks (PyTorch, TensorFlow, JAX) and MLsystem internals
• Proficiency in 2+ programming languages (e.g., Python, Go, C++, Rust, Java, Scala)
• Extensive experience with cloud-native technologies at scale: Kubernetes, Docker,AWS/GCP/Azure, CI/CD pipelines
• Familiarity with GPU programming and high-performance computing concepts (CUDA,distributed training, model parallelism)
• Demonstrated ability to drive cross-team/cross-org technical alignment and influencewithout authority
• Excellent communication skills — ability to articulate complex technical concepts todiverse audiences
• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
岗位亮点：
???? Global Reach
Open to global tech talent, not geo-restricted
Explicitly requires experience in globally distributed teams across time zones
Remote/hybrid options — very appealing to international candidates
???? AI + Infrastructure Dual Focus
Rare intersection role: not pure AI research, not pure infra — the bridge between both
Covers the hottest areas: LLM infrastructure (training, fine-tuning, RLHF, efficient serving)
GPU cluster management & heterogeneous compute (A100/H100, CUDA, NCCL) — seriously hard-core
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