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AI Engineer

Zoom
Seattle, USA
Full-time
Entry · 0–3 years experience
Onsite
Discovered 1 weeks ago
pythonpytorch

Full Job Posting

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 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.

Qualifications

  • 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
  • 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
  • $137,700.00 - $275,400.00
  • 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!
  • 11/13/26
  • 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.

Preferred Qualifications

  • A degree in Computer Science, Computer Engineering or a related field (MS or PhD preferred), or equivalent practical experience
  • Preferred
  • 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.

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