AI Researcher - Inference Optimization - remote job

Featherless AI is hiring a AI Researcher - Inference Optimization. The role sits in Other jobs and is fully remote. The company places no restriction on where the candidate lives, so you can apply from anywhere.

The main tools named in the advert are: Python, Machine Learning. Your CV should show concrete examples of exactly these skills.

The employer did not publish a figure; that is settled at interview. The advert sets no condition on working hours.

This vacancy passed an automated check: the list excludes any advert requiring a foreign work permit, visa sponsorship, a particular citizenship, or residence in a specific country.

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

Originally posted on Himalayas