AI Researcher — Inference Optimization
At a glance
Mid-level AI Research role at Featherless AI. Remote · full-time.
Pay not stated AI Research salary
aimlroles summary, based on the employer's posting.
What you'll do
- Develop methods that improve inference speed and resource use for large neural networks
- Tune model behavior through quantization, pruning, KV-cache changes and architecture-aware simplification
- Build system-level improvements such as dynamic batching, kernel fusion and multi-GPU inference
- Benchmark workloads on hardware accelerators, then work with engineers to put improvements into production
What you bring
- A strong foundation in machine learning, deep learning or AI systems
- Hands-on work optimizing inference for large-scale models
- Proficiency in Python and modern ML frameworks, such as PyTorch
- Experience with inference tools such as Triton, TensorRT or another listed platform
Who this fits
This role suits a mid-level AI researcher who can turn experiments into deployable inference improvements and communicate results clearly. The position is remote.
From the employer
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