Machine Learning Engineer — Inference Optimization

Featherless AI; Poland; On-site; PLN 255,754 - 383,631; Full time

Benefits offered by this job

Competitive compensation

Meaningful equity at Series A

Real ownership over performance-critical systems

Job summary

A pioneering AI tech company in Poland is seeking a Machine Learning Engineer to enhance model inference performance at scale. You will optimize latency and cost for large-scale ML models, collaborate with research engineers to deploy architectures, and profile inference pipelines to ensure reliability. The ideal candidate has strong experience in ML optimization, a solid grasp of deep learning concepts, and is comfortable in fast-paced startup settings. Competitive compensation and equity options are offered.

Qualifications

• Strong experience in ML inference optimization or high-performance ML systems.
• Solid understanding of deep learning internals like attention and memory layout.
• Hands-on experience with PyTorch (or similar) and model deployment.
• Familiarity with GPU performance tuning techniques.

Responsibilities

• Optimize inference latency and cost for large-scale ML models in production.
• Profile GPU/CPU inference pipelines for memory and process bottlenecks.
• Collaborate with research engineers to productionize new model architectures.
• Benchmark performance across hardware like NVIDIA and AMD GPUs.

Skills

ML inference optimization

Deep learning internals

PyTorch or similar

GPU performance tuning

Scaling inference for real users

Startup environments

Tools

CUDA

Triton

TensorRT

Job description

About the Role

We’re looking for a Machine Learning Engineer to own and push the limits of model inference performance at scale. You’ll work at the intersection of research and production—turning cutting‑edge models into fast, reliable, and cost‑efficient systems that serve real users.

This role is ideal for someone who enjoys deep technical work, profiling systems down to the kernel/GPU level, and translating research ideas into production‑grade performance gains.

What You’ll Do

• Optimize inference latency, throughput, and cost for large-scale ML models in production

• Profile and bottleneck GPU/CPU inference pipelines (memory, kernels, batching, IO)

• Implement and tune techniques such as:

  • Quantization (fp16, bf16, int8, fp8)

  • KV‑cache optimization & reuse

  • Speculative decoding, batching, and streaming

  • Model pruning or architectural simplifications for inference

• Collaborate with research engineers to productionize new model architectures

• Build and maintain inference‑serving systems (e.g. Triton, custom runtimes, or bespoke stacks)

• Benchmark performance across hardware (NVIDIA / AMD GPUs, CPUs) and cloud setups

• Improve system reliability, observability, and cost efficiency under real workloads

What We’re Looking For

• Strong experience in ML inference optimization or high‑performance ML systems

• Solid understanding of deep learning internals (attention, memory layout, compute graphs)

• Hands‑on experience with PyTorch (or similar) and model deployment

• Familiarity with GPU performance tuning (CUDA, ROCm, Triton, or kernel‑level optimizations)

• Experience scaling inference for real users (not just research benchmarks)

• Comfortable working in fast‑moving startup environments with ownership and ambiguity

Nice to Have

• Experience with LLM or long‑context model inference

• Knowledge of inference frameworks (TensorRT, ONNX Runtime, vLLM, Triton)

• Experience optimizing across different hardware vendors

• Open‑source contributions in ML systems or inference tooling

• Background in distributed systems or low‑latency services

Why Join Us

• Real ownership over performance‑critical systems

• Direct impact on product reliability and unit economics

• Close collaboration with research, infra, and product

• Competitive compensation + meaningful equity at Series A

• A team that cares about engineering quality, not hype