Automotive Application Engineer

About Us

Axelera AI https://www.axelera.ai is not your regular deep-tech company. We are creating the next-generation AI platform to support anyone who wants to help advancing humanity and improve the world around us.

In just five years, we have raised a total of $450 million and have built a world-class team of 250+ employees (including 60+ PhDs with more than 40,000 citations), both remotely from 20 different countries and with offices in Belgium, France, Switzerland, Italy, the UK, headquartered at the High Tech Campus in Eindhoven, Netherlands.

We have also launched our Metis™ AI Platform, which achieves a 3-5x increase in efficiency and performance, and have visibility into a strong business pipeline exceeding $100 million.

Our unwavering commitment to innovation has firmly established us as a global industry pioneer.

Are you up for the challenge?

Position Overview

Axelera AI is looking for a hands-on Automotive Application Engineer to test and adapt Axelera's full-stack edge-AI solution — accelerator silicon and its software toolchain — against automotive market requirements.

Working inside the Automotive team, you will turn market and customer requirements into testable criteria, then verify the solution against them: porting and quantising automotive AI workloads, characterising performance and power, benchmarking against competing platforms, and documenting the gap between what the stack does today and what automotive programs demand. You will also contribute to collaborative R&D programs and automotive consortia — owning technical work packages, building demonstrators, and feeding that work into the internal roadmap.

Reporting to the Head of Automotive, you will work closely with the Principal Automotive Solution Architect, the software and AI R&D teams, Product Management and the Functional Safety Manager.

Key responsibilities:

Solution Validation Against Market and Customer Requirements

• Translate market and customer requirements into testable evaluation criteria, verify the Axelera stack against them, and maintain a current requirements-versus-capability view across releases, with internal evaluation reports for Automotive, Product and R&D.

Benchmarking Against Automotive Use Cases

• Define and run benchmarks around real automotive use cases (ADAS perception, surround view/parking, BEV/occupancy, DMS/OMS, sensor fusion), expressed in automotive terms (FPS per stream, latency, accuracy at the required operating point).
• Port, quantize and optimize automotive workloads onto Axelera, build reproducible benchmark suites under automotive-realistic conditions, and run competitive comparisons — labeling every result by silicon revision, sample grade and SDK version.
• Maintain benchmark automation, regression tracking and dashboards.

Automotive Adaptation of the Full Stack

• Identify and close gaps between the current stack and automotive expectations (toolchain, runtime, OS/middleware integration, determinism, diagnostics), and prototype runtime integration into automotive environments (Linux/QNX, AUTOSAR Adaptive, Android Automotive OS, ROS 2).
• Feed automotive-specific requirements and thermal/duty-cycle profiles into SDK/product roadmaps and functional safety work.

Safety-Qualified Software Stack (ASIL-B / ASIL-D)

• Contribute hands-on to the ASIL-B/ASIL-D software stack (safety runtime, diagnostics, monitoring) alongside R&D and the Functional Safety Manager, verifying it against realistic customer integration scenarios.
• Assess integration effort for a Tier-1/OEM and feed gaps back as prioritized findings.

Collaborative R&D Programs and Consortia

• Own or co-own technical work packages in collaborative R&D projects and automotive/edge-AI consortia — deliverables, milestones, demonstrators — coordinating with OEM/Tier-1/research partners and keeping the work aligned with the internal roadmap.

Market and Technical Intelligence

• Track automotive AI/ADAS trends and competitive benchmarks, and translate findings into prioritized recommendations for Product and Engineering.

Qualifications:

Required

• 4–8 years in embedded software or application engineering, in automotive or automotive semiconductors, with strong embedded C and Python for tooling/test automation.
• Real-time systems and safety-qualified development in an ASIL-B/ASIL-D context (ISO 26262 requirements, safety mechanisms, diagnostics, supporting evidence).
• Embedded development on constrained targets (board bring-up, drivers, BSPs) using standard automotive toolchains (cross-compilation, trace/debug, CI, MISRA), on embedded Linux with exposure to QNX or AUTOSAR.
• Validation on target hardware: bench/HIL testing and characterization across the automotive temperature range.
• Structured, requirements-to-evidence mindset and clear technical writing.
• Fluent English; ~15% travel for consortium meetings and partner labs.

Highly Appreciated

• Exposure to deep-learning …