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