AI Researcher — Inference Optimization

Featherless AI; Poland; On-site; PLN 60,000 - 80,000; Full time

Job summary

A leading AI research firm in Poland seeks an AI Researcher to optimize inference systems for large-scale machine learning models. The ideal candidate has a strong background in machine learning and deep learning, along with experience in optimizing inference performance. Responsibilities include designing evaluation pipelines, collaborating with engineering teams, and translating research insights into production-ready systems. Familiarity with tools like PyTorch and Triton is essential. This role offers a unique opportunity to drive innovation in AI systems.

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.

Responsibilities

• Research and develop techniques to optimize inference performance.
• Improve latency, throughput, memory efficiency, and cost per inference.
• Design and evaluate model-level optimizations.

Skills

Machine learning

Deep learning

Inference optimization

Python

Communication skills

Tools

PyTorch

Triton

TensorRT

ONNX Runtime

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