AI Researcher — Inference Optimization
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Before you apply
LocationRemote (world)
Work moderemote
Typefull-time
SenioritySenior
DepartmentResearch
Company size51–200 people
PostedJan 23, 2026 · 9mo ago
Verified live1d ago
May no longer be actively hiring
Posted by the company on Jan 23, 202671% of this company's open roles have been listed for 90+ days
At a glanceSummarised by Seekless from the posting.
Strong background in machine learning, deep learning, or AI systems
Hands-on experience optimizing inference for large-scale 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 clearly
Experience deploying production inference systems at scale
Familiarity with distributed and multi-GPU inference
Experience contributing to open-source ML or inference frameworks
Authorship or co-authorship of peer-reviewed research papers in machine learning, systems, or related fields
Experience working close to hardware (CUDA, ROCm, profiling tools)
Skills
Python
PyTorch
Triton
TensorRT
vLLM
ONNX Runtime
CUDA
ROCm
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.
•
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 clearly.
Preferred / Nice-to-Have Qualifications
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Experience deploying production inference systems at scale.
•
Familiarity with distributed and multi-GPU inference.
•
Experience contributing to open-source ML or inference frameworks.
•
Authorship or co-authorship of peer-reviewed research papers in machine learning, systems, or related fields.
•
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