Inference Stack Engineer
At EER Poland · Gdańsk; Gdansk, Poland
- Engineering
Where you’ll work
Hybrid, based in Gdańsk
“Gdańsk”
From the location.
“Gdansk, Poland”
From the job board field.
The job board lists it as hybrid.
Hybrid means some days in the office. The listing says how many, and where.
Report a mistake
- Posted
- 3 June 2026
- First seen by us
- 12 October 2026
- Last checked
- 29 hours ago
Job description
From EER Poland’s job post, as they wrote it.
Inference Stack Engineer
(AI Systems / Compiler & Runtime)
We are building a next-generation AI inference stack designed for high-performance execution on modern and custom compute architectures. Our mission is to deliver industry-leading low-latency and high-throughput AI systems by designing and optimizing the full execution path — from model representation to hardware-level execution.
This is a deeply technical role at the intersection of compiler systems, AI runtimes, and high-performance computing.
Read the full description
You will work on core infrastructure that defines how modern AI models are executed efficiently at scale.
What you will do
* Design and build components of an AI inference stack, from high-level model representation to low-level execution
* Develop and extend a Python-based DSL for expressing AI workloads and kernels
* Work on compiler infrastructure including:
* IR design and transformation pipelines
* graph lowering and optimization passes
* backend code generation for target execution environments
* Optimize model execution for:
* latency
* throughput
* memory efficiency
* numerical stability
* Contribute to runtime systems responsible for model execution and scheduling
* Profile and analyze inference workloads to identify system bottlenecks
* Collaborate closely with hardware and systems engineers on execution efficiency
* Influence architecture decisions for next-generation AI execution platforms
What we are looking for
* Strong software engineering background (C++ and Python)
* Experience with performance-critical systems or compiler-related work
* Understanding of AI model execution (especially transformers / LLMs)
* Familiarity with compute graphs, tensor operations, or execution frameworks
* Ability to analyze complex systems end-to-end (model → runtime → hardware)
* Experience working with large codebases and system-level debugging
* Strong communication skills and ability to work in cross-functional teams
Nice to have
* Experience with compiler frameworks such as:
* LLVM
* MLIR
* Triton
* TVM
* XLA
* Experience contributing to deep learning frameworks (PyTorch, TensorFlow, JAX)
* Understanding of GPU or accelerator execution models
* Experience with kernel optimization or operator-level performance tuning
* Knowledge of distributed inference systems (e.g. NCCL, RPC-based serving)
* Familiarity with hardware-aware optimizations (memory hierarchy, vectorization, scheduling)
What we offer
* Work on the core execution layer of modern AI systems
* Direct impact on inference performance of large-scale AI workloads
* Collaboration with experts in compilers, systems, and AI infrastructure
* Highly technical environment with strong engineering autonomy
* Opportunity to shape the architecture of a next-generation inference stack
* Competitive compensation and flexible working model
Why this role is different
This is not a typical ML engineering or application role.
You will not be training models.
You will be working on how models actually run efficiently, at scale, across compute systems, shaping the performance layer that sits between AI models and hardware.
Get jobs like this by email
Once a week, we email the new remote jobs open where you live, each with the line that proves it. Every email has a one-click unsubscribe.
