AI Researcher — Inference Optimization
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 283d ago
The date the source published, not the day we noticed it (2026-01-23). Last seen at its source 5h ago.
We have tracked this listing since 7 Aug 2026 (87 days). The employer's own board has carried it every time we have read it since 19 Aug, when our records begin, most recently 5 hours ago.
Is it remote?
Remote (world)
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
Available worldwide
The description states no restriction of its own. This is the source's own tag.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
Ashby employer's own board first seen 87d ago · last seen 5h ago
The listing
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
Research and develop 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).
Benchmark inference workloads across hardware accelerators.
Collaborate with engineering teams to deploy optimized inference pipelines.
Translate research insights into production-ready improvements.
Required Qualifications
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.
Preferred / Nice-to-Have Qualifications
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
Measurable gains in latency, throughput, and cost efficiency.
Optimized inference systems running reliably in production.
Research ideas successfully translated into deployable systems.
Clear benchmarks and documentation that inform product decisions.
Relevant Research Areas (Bonus)
Long-context inference optimization
Speculative decoding
KV-cache compression and paging
Efficient decoding strategies
Hardware-aware inference design