AI Software Engineer

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Our people make all the difference in our success.

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.

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.

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 eng 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  

• Advanced degrees (Master’s or PhD) are advantageous

• 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, profiling with Nsight or equivalent

• 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:

• 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.

Information about Zoom’s benefits is on our careers page here . 

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.

Good news – this job posting is more like a marathon, not a sprint, so it could be available for a while! We're on the lookout for awesome folks to join Zoom in various similar roles. No need to rush, just hit us up whenever you're ready to apply. We're always keeping an eye out for amazing talent!

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