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Software Engineer, ML Infrastructure

ora computing • Austria
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AI Summary

This role focuses on structuring and scaling ora computing's software stack, particularly in the algorithm and inference layers. Key responsibilities include designing and refactoring core libraries for compression pipelines and building internal tooling for team efficiency. Requires strong code design opinions, GPU experience, and production-grade Python skills.

Key Highlights
Owns the software stack architecture and scalability.
Designs and implements automated compression pipelines.
Sets engineering standards and mentors the team.
Key Responsibilities
Design and refactor core libraries (pruning, quantization, retraining, evaluation) into clean, well-scoped packages.
Build internal tooling for team velocity and stability (CI, benchmarks, reproducible runs).
Integrate compression output with inference engines (vLLM, TensorRT-LLM, llama.cpp) and customer deployment targets.
Set the engineering bar for the team as new hires join.
Technical Skills Required
Python GPU Code Design
Benefits & Perks
€70–120k base + equity
Visa sponsorship
Relocation support
Nice to Have
Open-source contributions to ML infrastructure (vLLM, llama.cpp, transformers, TensorRT-LLM, PyTorch internals).
CUDA, Triton, or kernel-level work.
Experience designing a library from scratch that other engineers ended up using.
Familiarity with model serving and inference optimization.

Job Description


THE ROLE

About The Role

You'll own how our software stack is built. Today the codebase reflects four people moving fast, it works, but it needs structure. Your job is to give it that structure: well-designed libraries, robust packages, environments, the kind of codebase that scales as we grow the team and ship more to customers.

This is not a glue-code role. You'll work between the algorithm and inference layer: designing a compression pipeline that is fully automated and takes target runtimes into account. You'll design the abstractions our compression pipeline runs on and make them fast.

What You'll Do

Your impact

  • —Design and refactor our core libraries — pruning, quantization, retraining, evaluation — into clean, well-scoped packages.
  • —Build the internal tooling that lets the team move quickly without breaking things — CI, benchmarks, reproducible runs.
  • —Integrate our compression output with inference engines (vLLM, TensorRT-LLM, llama.cpp) and customer deployment targets.
  • —Set the engineering bar for the team as we hire.

What You Bring

What we're looking for

  • —Bachelor's/Master's in computer science or equivalent, plus 2+ years of professional software engineering.
  • —Strong opinions about code design. You know what a well-structured library looks like and why.
  • —GPU experience — memory hierarchy, kernels, what bottlenecks performance — even if you don't write CUDA daily.
  • —Production-grade Python. You write code others can read, extend, and trust.
  • —You finish things and you care about the codebase you leave behind.

NICE TO HAVE

  • —Open-source contributions to ML infrastructure (vLLM, llama.cpp, transformers, TensorRT-LLM, PyTorch internals).
  • —CUDA, Triton, or kernel-level work.
  • —Experience designing a library from scratch that other engineers ended up using.
  • —Familiarity with model serving and inference optimization.

PRACTICAL

  • —Vienna-based. Hybrid or fully remote.
  • —Working language is English.
  • —We sponsor visas and support relocation.
  • —Compensation: €70–120k base + equity. Austrian minimum disclosed per Kollektivvertrag: €45,738/year.
  • —You'll set the engineering standards we hire against next.

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