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Open nowPosted 27 days ago

Performance Engineer - Inference

Zenteiq Aitech10 open roles

Where
Bengaluru
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Your applicationOpen nowPerformance Engineer - InferenceZenteiq Aitech · Bengaluru

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  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 27 days ago

The posting

About the Role

We are looking for a Performance Engineer, Inference to understand and improve the systems that

serve our foundation models. Inference is a tightly coupled system spanning model execution, serving

runtimes, distributed systems, accelerators, scheduling, memory, and reliability. You will measure the

system end to end, identify the highest-leverage performance gaps, and work across teams to close

them while preserving correctness.

Responsibilities

• Run cross-layer performance investigations across throughput, latency, memory efficiency,

reliability, and cost.

• Build profiling, benchmarking, and observability tools that make inference performance

measurable and explainable.

• Identify bottlenecks across model servers, batching and scheduling, distributed execution, memory

systems, and accelerators.

• Partner with model, platform, and infrastructure teams to prioritize and land high-impact

optimizations.

• Validate that performance improvements preserve model quality and numerical correctness.

Minimum Qualifications

• Hands-on experience profiling and optimizing ML systems or other performance-critical production

systems.

• Production experience with at least one modern inference stack such as vLLM, SGLang,

TensorRT-LLM, NVIDIA Dynamo, or an equivalent serving runtime.

• Experience serving or operating large models across accelerator-backed infrastructure, including

multi-GPU or multi-accelerator systems.

• Strong Python skills and the ability to read, instrument, and modify large production codebases.

• Solid understanding of transformer inference, distributed systems, latency/throughput trade-offs,

and accelerator performance fundamentals.

Preferred Qualifications

• Experience with large-scale or multi-node inference, including tensor, pipeline, data, or expert

parallelism.

• Experience with GPUs, TPUs, NPUs, or other ML accelerators and associated profiling tools.

• Experience with quantization, low-precision inference, KV-cache optimization, speculative

decoding, or long-context serving.

• Experience contributing to or modifying inference runtimes, kernels, compilers, or distributed

serving components.

• Experience optimizing inference for constrained or on-device environments.

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