Automotive Application Engineer

Axelera AI

. Test and adapt Axelera AI’s full-stack edge-AI solution, including accelerator silicon and software toolchain, against automotive market requirements .

Posted 9/21/2026full-timeNetherlandsMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in embedded software development for automotive applications, with a strong focus on ASIL-B/ASIL-D safety standards and real-time systems. Proficient in utilizing embedded C and Python for tooling and test automation, alongside experience in automotive toolchains and validation processes.

Highest-signal resume keywords

Embedded C ProgrammingPython Test AutomationASIL-B/ASIL-D DevelopmentISO 26262 ComplianceEmbedded Linux Experience

ATS Keywords

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Hard Skills

Embedded Software DevelopmentReal-Time SystemsToolchain IntegrationBenchmarking and ValidationModel DeploymentQuantization TechniquesAutomotive DiagnosticsSafety MechanismsCross-CompilationHIL Testing

Soft Skills

Technical WritingCollaborationProblem-SolvingCommunication

Tools & Technologies

AUTOSAR AdaptiveAndroid Automotive OSQNXPyTorchONNXSOME/IPDDS

Industry Keywords

Automotive SemiconductorsADAS PerceptionISO 21434A-SPICEVehicle E/E Architectures

Tech Stack

Tools & technologies

AndroidLinuxPythonPyTorch

About the role

Key responsibilities & impact

Test and adapt Axelera AI’s full-stack edge-AI solution, including accelerator silicon and software toolchain, against automotive market requirements
Translate market and customer requirements into testable evaluation criteria
Verify the Axelera stack against requirements and maintain a requirements-versus-capability view across releases
Produce internal evaluation reports for Automotive, Product, and R&D
Define and run benchmarks for ADAS perception, surround view/parking, BEV/occupancy, DMS/OMS, and sensor fusion
Port, quantize, and optimize automotive workloads onto Axelera
Build reproducible benchmark suites under automotive-realistic conditions
Run competitive comparisons and label results by silicon revision, sample grade, and SDK version
Maintain benchmark automation, regression tracking, and dashboards
Identify and close gaps in the toolchain, runtime, OS/middleware integration, determinism, and diagnostics
Prototype runtime integration into Linux/QNX, AUTOSAR Adaptive, Android Automotive OS, and ROS 2 environments
Feed automotive requirements and thermal/duty-cycle profiles into SDK/product roadmaps and functional safety work
Contribute to ASIL-B/ASIL-D software stack development, including safety runtime, diagnostics, and monitoring
Assess Tier-1/OEM integration effort and provide prioritized findings
Own or co-own technical work packages in collaborative R&D projects and automotive/edge-AI consortia
Deliver milestones, demonstrators, and technical deliverables while coordinating with OEM, Tier-1, and research partners
Track automotive AI/ADAS trends and competitive benchmarks and translate findings into recommendations for Product and Engineering
Report to the Head of Automotive and collaborate with the Principal Automotive Solution Architect, software and AI R&D teams, Product Management, and Functional Safety Manager

Requirements

What you’ll need

4–8 years in embedded software or application engineering in automotive or automotive semiconductors
Strong embedded C and Python for tooling/test automation
Real-time systems and safety-qualified development in an ASIL-B/ASIL-D context
Knowledge of ISO 26262 requirements, safety mechanisms, diagnostics, and supporting evidence
Embedded development on constrained targets, including board bring-up, drivers, and BSPs
Experience with standard automotive toolchains: cross-compilation, trace/debug, CI, and MISRA
Embedded Linux experience with exposure to QNX or AUTOSAR
Validation on target hardware, including bench/HIL testing and characterization across the automotive temperature range
Structured requirements-to-evidence mindset and clear technical writing
Fluent English
Ability and willingness to travel approximately 15% for consortium meetings and partner labs
Experience with deep-learning frameworks such as PyTorch and ONNX, model deployment on embedded targets, and quantization/graph compilation concepts is highly appreciated
Familiarity with computer-vision/camera-based automotive perception workloads is highly appreciated
Experience in collaborative R&D projects is highly appreciated
Deeper experience with AUTOSAR Adaptive, Android Automotive OS, SOME/IP, DDS, or sensor interfaces is highly appreciated
Awareness of ISO 21434 and A-SPICE and exposure to vehicle E/E architectures are highly appreciated
An additional European language is highly appreciated

Benefits

Comp & perks

Pension plan
Extensive employee insurances
Option to get company shares
Flexible working arrangement
Option to work remotely from a European country
Option to work from Axelera AI offices
Relocation support to Italy or the Netherlands
Open, creative, and inclusive culture
Collaborative ownership and freedom with responsibility