In this role, you will:

• Establish and lead the SRE / production engineering practice for the Common Services organization, including standards for reliability, incident management, and on-call, in partnership with the central Product Engineering organization.
• Develop an Operational Excellence strategy that focuses on not only improving system performance but also monitoring and reducing operational toil
• Partner with engineering and product teams to define SLOs, SLIs, and error budgets for critical Common Services, and ensure these become part of how teams plan and make tradeoffs.
• Own and improve the incident management lifecycle for Common Services, including on-call rotations, escalation paths, incident tooling, post-incident reviews, and follow-through on corrective actions.
• Drive the observability strategy (metrics, logs, traces, dashboards, alerts) for Common Services, ensuring we have actionable visibility into the health, performance, and capacity of key systems.
• Collaborate with engineering leads to design and review architectures for reliability, scalability, resilience, and operability, including failure modes, redundancy, and graceful degradation.
• Lead efforts to automate and harden operational workflows, including deployments, rollbacks, configuration management, change management, and routine maintenance tasks.
• Build strong, trust-based relationships with partner teams and stakeholders, becoming a go-to leader for production readiness and operational risk within Common Services.
• Hire, mentor, and develop SRE and production engineering talent, fostering a culture of continuous improvement, learning from incidents, and humane on-call.
• Partner with other SRE and production engineering leaders across CoreWeave to align on global practices, tools, and reliability goals, representing the needs and constraints of Common Services.

You will be responsible for defining the reliability strategy, processes, and standards for the Common Services portfolio and driving consistent, high-quality operational practices across multiple teams.

The base salary range for this role is $206,000 to $303,000.

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Microsoft AI are looking for a talented Senior Software Engineer at their Beijing office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI technology. You'll work directly with leadership to shape the company's direction in the AI market.

About the Role

We are seeking an expert Senior GPU Engineer to join our AI Infrastructure team. In this role, you will architect and optimize the core inference engine that powers our large-scale AI models. You will be responsible for pushing the boundaries of hardware performance, reducing latency, and maximizing throughput for Generative AI and Deep Learning workloads. You will work at the intersection of Deep Learning algorithms and low-level hardware, designing custom operators and building a highly efficient training/inference execution engine from the ground up.

Accountabilities

• Custom Operator Development: Design and implement highly optimized GPU kernels (CUDA/Triton) for critical deep learning operations (e.g., FlashAttention, GEMM, LayerNorm) to outperform standard libraries.
• Inference Engine Architecture: Contribute to the development of our high-performance inference engine, focusing on graph optimizations, operator fusion, and dynamic memory management (e.g., KV Cache optimization).

The Candidate we're looking for

Experience:

• 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python.

Technical skills:

• Expertise in the CUDA programming model and NVIDIA GPU architectures (specifically Ampere/Hopper).
• Deep understanding of the memory hierarchy (Shared Memory, L2 cache, Registers), warp-level primitives, occupancy optimization, and bank conflict resolution.

Personal attributes:

• Proven ability to navigate and modify complex, large-scale codebases (e.g., PyTorch internals, Linux kernel).

Benefits

• Starting January 26, 2026, Microsoft AI employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week.

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