Role Overview
We are seeking an AI Researcher with deep experience in inference optimization to design, evaluate, and deploy high-performance inference systems for large-scale machine learning models. You will work at the intersection of model architecture, systems engineering, and hardware-aware optimization, improving latency, throughput, and cost efficiency across real-world production environments.
Key Responsibilities
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Research and develop techniques to optimize inference performance for large neural networks.
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Improve latency, throughput, memory efficiency, and cost per inference.
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Design and evaluate model-level optimizations (quantization, pruning, KV-cache optimization, architecture-aware simplifications).
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Implement systems-level optimizations (dynamic batching, kernel fusion, multi-GPU inference, prefill vs decode optimization).
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Benchmark inference workloads across hardware accelerators.
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Collaborate with engineering teams to deploy optimized inference pipelines.
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Translate research insights into production-ready improvements.
Required Qualifications
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Strong background in machine learning, deep learning, or AI systems.
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Hands-on experience optimizing inference for large-scale models.
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Proficiency in Python and modern ML frameworks (e.g., PyTorch).
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Experience with inference tooling (e.g., Triton, TensorRT, vLLM, ONNX Runtime).
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Ability to design experiments and communicate results clearly.
Preferred / Nice-to-Have Qualifications
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Experience deploying production inference systems at scale.
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Familiarity with distributed and multi-GPU inference.
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Experience contributing to open-source ML or inference frameworks.
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Authorship or co-authorship of peer-reviewed research papers in machine learning, systems, or related fields.
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Experience working close to hardware (CUDA, ROCm, profiling tools).
What Success Looks Like
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Measurable gains in latency, throughput, and cost efficiency.
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Optimized inference systems running reliably in production.
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Research ideas successfully translated into deployable systems.
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Clear benchmarks and documentation that inform product decisions.
Relevant Research Areas (Bonus)
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Long-context inference optimization
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Speculative decoding
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KV-cache compression and paging
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Efficient decoding strategies
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Hardware-aware inference design
Originally posted on Himalayas
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