AI Researcher — Inference Optimization
Role Overview
As an AI Researcher specializing in inference optimization, you will focus on designing and deploying high-performance inference systems for large-scale machine learning models. This senior-level role involves collaborating with engineering teams, implementing optimizations, and translating research insights into production-ready improvements, ultimately driving measurable gains in latency and cost efficiency.
Perks & Benefits
This fully remote position offers flexibility in work hours, allowing you to collaborate across time zones. Featherless AI values a culture of innovation and encourages career growth through hands-on experience with cutting-edge technologies. You'll have the opportunity to contribute to impactful projects while working with a team of experts in the field.
Full Job Description
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
Similar jobs
Found 6 similar jobs
