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Intern – ML Inference Performance Engineer
Axelera AI. Develop a repeatable evaluation methodology for ML inference performance .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing evaluation methodologies for machine learning inference performance, with hands-on experience in Python, C/C++, and benchmarking tools. Proficient in documenting findings and communicating results effectively while maintaining a strong understanding of deep learning models and performance metrics.
Highest-signal resume keywords
Python Development ExperienceC/C++ KnowledgeExperience With Inference ToolsFamiliarity With Deep Learning ConceptsProficiency With Linux And Bash Scripting
ATS Keywords
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Hard Skills
Machine Learning Inference PerformanceBenchmarking Tools DevelopmentEnd-To-End Computer Vision PipelinesPerformance And Latency EvaluationDeep Learning Model QuantizationVersion Control With GitGStreamer KnowledgeDocker ProficiencyEmbedded Hosts ExperienceAgentic AI Development
Soft Skills
Proficient Written And Verbal CommunicationGood Organizational SkillsAbility To Document Findings Clearly
Tools & Technologies
TensorRTONNXAI Accelerator ProductsVendor SDKsToolchains
Industry Keywords
Performance BottlenecksCross-Platform ComparisonsInference Pipeline CharacterizationLab Host MaintenanceLLM Benchmarking Concepts
Tech Stack
Tools & technologiesDockerLinuxPythonPyTorchC++
About the role
Key responsibilities & impact- Develop a repeatable evaluation methodology for ML inference performance
- Investigate performance bottlenecks at hardware and software levels
- Develop and improve benchmarking tools covering throughput, latency, power, and accuracy
- Define reproducible procedures and establish a standardised results format for cross-platform comparisons
- Maintain a dedicated dashboard for performance visualisations
- Research and evaluate AI accelerator products from various vendors
- Gain hands-on experience with vendor SDKs, toolchains, flexibility, and limitations
- Track model support across platforms to identify strengths, gaps, and improvement areas
- Characterise full inference pipelines, including host-device transaction overhead and end-to-end performance metrics
- Ensure equivalent pipeline configurations across platforms using frameworks such as GStreamer
- Set up and maintain lab hosts across multiple hardware platforms
- Support onboarding of new evaluation hardware
- Synthesise findings into structured reports informing engineering and roadmap decisions
Requirements
What you’ll need- Currently enrolled in the final years of a Bachelor's programme or in a Master's programme in Computer Engineering, Electrical Engineering, Computer Science, or a related field
- Position may be carried out as a Master's thesis project
- Python development experience
- C/C++ knowledge
- Experience with end-to-end computer vision pipelines
- Familiarity with benchmarking concepts, including performance and latency
- Experience with inference tools, APIs, or SDKs such as TensorRT
- Familiarity with deep learning model concepts including quantization, ONNX, and PyTorch
- Development experience using agentic AI
- Knowledge of version control with Git
- Familiarity with LLM benchmarking concepts
- Proficiency with Linux, Bash scripting, and Docker
- Hands-on experience with embedded hosts
- Proficient written and verbal communication skills in English
- Ability to document findings clearly and precisely
- Good organizational skills
- GStreamer knowledge (nice to have)
- Basic GUI design experience (nice to have)
- Necessary work authorization for the Eindhoven location, valid for the entire internship duration
Benefits
Comp & perks- Pension plan
- Extensive employee insurances
- Option to get company shares
- Open culture supporting creativity and continual innovation
- Collaborative ownership and freedom with responsibility
- Equal-opportunity, diverse, warm, and inclusive environment