Staff AI Engineer
About The Position
You'll design, implement, and own the inference systems that serve Zoom's AI models at production scale -- across real-time communication, vision, and language workloads. You'll be hands-on with kernel-level optimisation, inference framework internals, and production serving infrastructure, working closely with research and platform teams to push the boundary on latency, throughput, and cost. You will join a dynamic AI Infrastructure team focused on enabling high-performance AI across Zoom's products and services. The team builds the core systems that support model training, deployment, and inference at scale, driving innovation in areas such as real-time communication, computer vision, and natural language understanding.
Requirements
- A Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related technical field, or equivalent practical experience
- 5+ years of software engineering experience, with significant time spent on inference systems or ML infrastructure at production depth
- Hands-on experience with at least one major inference framework: vLLM, TensorRT-LLM, SGLang, or ONNX Runtime (serving, not just export)
- GPU programming experience: CUDA kernel development, memory optimisation, and profiling with Nsight or equivalent tools
- Production experience serving LLMs or large vision models -- you've owned latency SLOs, debugged throughput regressions, and shipped optimisations that moved the needle
- Depth in at least two of: speculative decoding, continuous batching, KV cache design, quantisation pipelines, prefill/decode disaggregation
- Strong systems instincts in Python and C++; ability to read and modify framework internals
Nice To Haves
- Advanced degree (Master's or PhD) in a relevant technical field
- Experience with MoE models or 100B+ parameter deployments
- Familiarity with disaggregated serving architectures or multi-node inference
- Background in compiler-level optimisation (XLA, Triton, or similar)
Responsibilities
- Design and build high-performance inference serving systems for large-scale transformer and multimodal models (including 100B+ and MoE architectures)
- Implement and tune inference optimisations: speculative decoding, continuous batching, KV cache management, prefill/decode disaggregation, and quantisation (INT4/INT8/FP8)
- Contribute to and customise inference frameworks (vLLM, TensorRT-LLM, SGLang, or equivalent) for Zoom's production requirements
- Write and profile CUDA kernels and custom ops where framework-level optimisation is insufficient
- Own end-to-end deployment: from model packaging and serving API design to latency SLO monitoring and incident response
- Partner with research to translate model architecture changes into inference-efficient implementations
- Drive technical design and set the bar for inference engineering practices across the team
Benefits
- Total Direct Compensation philosophy that takes into consideration; base salary, bonus and equity value.
- Award-winning workplace culture
- Commitment to delivering happiness
- Variety of perks, benefits, and options to help employees maintain their physical, mental, emotional, and financial health
- Support work-life balance
- Contribute to their community in meaningful ways
- Opportunities to stretch your skills and advance your career in a collaborative, growth-focused environment
- Fair hiring practices that ensure every candidate is evaluated based on skills, experience, and potential
- Accommodation during the hiring process
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What This Job Offers
Job Type
Full-time
Career Level
Senior
