Staff AI Application Engineer - Heterogeneous Computing

axelera; United States; Remote; USD 180,000 - 260,000; Full time

Benefits offered by this job

Pension plan

Employee insurance

Stock options

Job summary

axelera is seeking a senior software engineer to lead the technical direction for ML model execution across CPUs and accelerators. The role covers model ingestion, graph compilation, runtime behavior, and end-to-end performance measurement.

You will design reusable tooling, profiling workflows, and CI processes, collaborating with compiler and runtime teams to scale performance on large, multi-model pipelines.

Qualifications

• 8+ years in systems, compilers, runtimes, or performance with ownership of system-level design.
• Experience with heterogeneous computing across CPUs and accelerators such as GPUs/NPUs/FPGA.
• Strong C++ and Python skills for toolchains and modeling workflows.
• Familiarity with ML graph representations and compiler concepts (capture, lowering, partitioning, memory planning).

Responsibilities

• Design a model enablement platform with reusable tools for partitioning, compiling, running, profiling, and verifying models on hardware.
• Build repeatable workflows to apply changes, compare performance against a reference, and detect regressions via CI.
• Define model capture/analysis/partitioning across accelerators and host CPUs; expose placement choices for users.
• Extend execution strategies to large models and multi-model pipelines with offloading and caching.
• Collaborate with compiler, runtime, and application teams to improve end-to-end performance through scheduling and data movement.
• Profile models, verify performance in full applications, and turn findings into reusable platform improvements.

Skills

C++

Python

System design

Performance optimization

Heterogeneous computing

CI/CD

AI tooling usage

Tools

MLIR

TVM

XLA

TorchInductor

IREE

Job description

Role overview

Set the technical direction and build software that enables machine learning models to run efficiently across specialized accelerators, host CPUs, and other available devices. This senior hands-on role spans model ingestion, graph compilation, runtime behavior, and application pipelines, with end-to-end performance as the goal. You’ll also build tools and verification workflows that help engineers and AI coding agents bring models up on real hardware and measure changes reliably.

Responsibilities

• Design a model enablement platform with reusable tools for partitioning, compiling, running, profiling, and verifying models on hardware.
• Build repeatable workflows that apply changes, compare performance with a reference, and detect regressions through testing and CI.
• Define how PyTorch or ONNX models are captured, analyzed, and partitioned across accelerator units, host CPUs, and available host accelerators; expose placement choices for users to inspect and adjust.
• Extend execution strategies to large models and multi-model pipelines, including techniques such as offloading and caching across device and host memory.
• Work with compiler, runtime, and application teams to improve complete-pipeline performance through scheduling, batching, overlapping work, and efficient data movement.
• Profile representative models, verify performance improvements in full applications, and turn findings into reusable platform improvements.

Requirements

• At least 8 years of engineering experience in systems, compilers, runtimes, or performance, including ownership of system-level design.
• Practical experience with heterogeneous computing across CPUs and accelerators such as GPUs, DSPs, NPUs, or FPGAs.
• Strong understanding of computer architecture, operating systems, parallel programming, and tradeoffs in compute, bandwidth, latency, and data movement.
• Familiarity with machine learning graph representations and compiler concepts such as graph capture, lowering, operator fusion, partitioning, and memory planning.
• Strong C++ and Python skills.
• Daily use of AI coding agents in engineering work, with the ability to test and critically review their output.

Nice to have

• Experience with MLIR, TVM, XLA, TorchInductor, IREE, or another compiler framework.
• Experience with an inference runtime, GPU programming, or SIMD optimization on Arm or x86.
• Familiarity with large-model execution techniques such as offloading, expert caching, speculative decoding, or pipelining across devices.
• Experience building agent environments, evaluation harnesses, differential tests, regression detection, or MCP-based tools.

Benefits and work setup

The role can be based remotely in a European country, including the UK, from an office in several European locations, or through relocation to Italy or the Netherlands. Candidates based in Belgium or Italy are prioritized.

• The compensation package includes a pension plan, employee insurance, and an option to receive company shares.