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 will build and optimize the inference systems that serve Zoom's AI models in production. Working alongside senior inference engineers, research and platform teams, you will profile serving workloads, find the bottlenecks that limit latency, throughput and cost, and ship the fixes. You will work close to the metal: inside inference frameworks, GPU profiles and serving infrastructure.
Responsibilities
- Build, tune and maintain components of Zoom's inference serving stack for transformer and multimodal models, including large and Mixture-of-Experts (MoE) architectures
- Implement and evaluate inference optimisations such as speculative decoding, continuous batching, KV cache management, parallelism layouts and quantisation (INT4/INT8/FP8)
- Extend and customise inference frameworks (vLLM, SGLang, TensorRT-LLM or equivalent) to support new models and Zoom's production requirements
- Profile GPU workloads with Nsight Systems or equivalent tools, root-cause performance regressions, and validate improvements with reproducible benchmarks
- Build and improve internal tooling for benchmarking, performance modelling and serving configuration
- Partner with researchers to bring new model architectures into production efficiently
- Contribute to on-call and production support for the services you build, with guidance from senior engineers
What we're looking for
- A degree in Computer Science, Computer Engineering or a related field (MS or PhD preferred), or equivalent practical experience
- 0-3 years of software engineering experience; internships, research and open-source work in ML systems count
- Hands-on experience with at least one inference framework (vLLM, SGLang, TensorRT-LLM or ONNX Runtime), at the level of serving and modifying it, not only exporting models
- Working knowledge of how LLM inference performs on GPUs: memory bandwidth, KV cache, batching, and communication across GPUs
- Experience profiling and debugging GPU performance, with evidence of an optimisation you measured and shipped
- Depth in at least one of: speculative decoding, continuous batching, KV cache design, quantisation, distributed or multi-GPU serving
- Strong programming skills in Python and C++, and comfort reading and changing large framework codebases
Preferred
- Merged contributions to vLLM, SGLang, TensorRT-LLM, PyTorch or similar projects
- CUDA, Triton or CUTLASS kernel development
- Experience serving MoE or 100B+ parameter models, or multi-node inference (NCCL, RDMA/RoCE)
- Graduate coursework or research in ML systems, compilers or high-performance computing
Salary Range or On Target Earnings:$137,700.00 - $275,400.00
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!
Anticipated Position Close Date:
11/13/26
Ways of WorkingOur 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.
BenefitsAs 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 Learn for more information.
