Staff AI Engineer

What You Can Expect

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.

About the Team

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.

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

What We're Looking For

• 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

Preferred

• 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)

Salary Range or On Target Earnings:

Minimum:

Maximum:

In addition to the base salary and/or OTE listed Zoom has a Total Direct Compensation philosophy that takes into consideration; base salary, bonus and equity value.

Note: Starting pay will be based on a number of factors and commensurate with qualifications & experience.

We also have a location based compensation structure;  there may be a different range for candidates in this and other locations

At Zoom, we offer a window of at least 5 days for you to apply because we believe in giving you every opportunity. Below is the potential closing date, just in case you want to mark it on your calendar. We look forward to receiving your application!

Ways of Working
Our structured hybrid approach is centered around our offices and remote work environments. The work style of each role, Hybrid, Remote, or In-Person is indicated in the job description/posting.

Benefits
As part of our award-winning workplace culture and commitment to delivering happiness, our benefits program offers a variety of perks, benefits, and options to help employees maintain their physical, mental, emotional, and financial health; support work-life balance; and contribute to their community in meaningful ways. Click for more information.