ML Compiler (Front-End) Engineer - Barcelona, Spain
--10xEngineers--
Type: Contract (6-9 months)
Location: Barcelona, Spain (Candidates must be authorized to work in Spain)
Role Overview
We are seeking a Front-End Compiler Engineer to design, develop, and scale the compiler front-end for our AI/ML stack. This role focuses on building Python-based model conversion pipelines that translate models from popular ML frameworks such as ONNX, TensorFlow, and PyTorch into our internal Intermediate Representation (IR).
The ideal candidate will work extensively on graph-level representations and optimizations, support modern deep learning architectures (including LLMs), and build robust testing infrastructure to ensure correctness, performance, and long-term maintainability of the compiler front-end.
Key Responsibilities
- Design, develop, and maintain Python-based front-end converter modules to ingest models from ONNX, TensorFlow, and PyTorch into an internal IR.
- Implement graph construction, transformation, and IR lowering pipelines as part of the compiler front-end.
- Analyze computation graphs and implement graph-level optimization passes, such as operator fusion, simplification, and canonicalization.
- Build and extend pattern-matching and graph-rewriting frameworks for scalable and maintainable optimizations.
- Work on model decomposition and conversion of key building blocks used in LLMs, including attention mechanisms, MLPs, normalization layers, and embeddings.
- Leverage and integrate tools from ONNX Runtime for model parsing, validation, and conversion workflows where applicable.
- Develop and maintain Python-based testing infrastructure for correctness validation, operator coverage, regression testing, and CI integration.
- Debug and resolve issues across model ingestion, conversion, graph optimization, and IR generation stages.
- Collaborate with backend compiler, runtime, and performance teams to ensure end-to-end model correctness and efficiency.
Required Skills & Experience
- Strong Python programming skills (mandatory) with an emphasis on clean, modular, maintainable, and well-tested code.
- Solid understanding of compiler fundamentals, including:
- Intermediate Representations (IRs)
- Graph-based computation models
- Transformation and optimization passes
- Hands-on experience with ML frameworks, including ONNX, TensorFlow, PyTorch, and exposure to Caffe.
- Practical experience in graph parsing, transformation, and optimization for ML models.
- Familiarity with modern ML architectures, particularly CNNs and Transformer-based models.
- Experience building or contributing to testing frameworks for compilers, ML systems, or large Python codebases.
- Strong debugging and problem-solving skills across complex, multi-stage pipelines.
Good to Have
- Familiarity with MLIR-based front-ends and dialects, such as:
- TOSA
- StableHLO
- Torch-MLIR
- Exposure to AI compiler stacks, hardware backends, or accelerator targeting.
- Experience working with large-scale models or production ML inference/training pipelines.
Note: 10xEngineers does not partner with third-party job portals, recruiters, or agencies to publish our job openings.
All open roles are listed on our careers page, and we encourage candidates to apply directly.