AI Accelerator Software Principal Engineer- Framework Integration

Invent the future with us.

Ampere is a semiconductor design company for a new era, leading the future of computing with an innovative approach to CPU design focused on high‑performance, energy efficient AI compute.

Ampere is a semiconductor design company for a new era, leading the future of computing with an innovative approach to CPU design focused on high‑performance, energy efficient AI compute. As a pioneer in the new frontier of energy efficient high‑performance computing, Ampere is part of the Softbank Group of companies driving sustainable computing for AI, Cloud, and edge applications. Join us at Ampere and work alongside a passionate and growing team - we’d love to have you apply!

As an AI Accelerator Software Principal Engineer – Framework Integration, you will lead the design and delivery of high‑performance, low‑latency deep learning inference solutions. You’ll help advance Ampere’s AI software stack by integrating and optimizing popular deep learning frameworks so models can run efficiently across data center and edge environments—meeting the performance and efficiency requirements of next‑generation AI workloads. You will operate at the intersection of software engineering, performance engineering, and hardware‑aware optimization, contributing to the full stack from model execution to accelerator‑ready kernel performance.

• Framework integration for accelerator backends:
  Integrate and optimize deep learning frameworks—such as PyTorch, ONNX, and llama.cpp—for the Ampere deep learning accelerator backend, enabling efficient and correct execution across a wide set of model types.
• End‑to‑end deep learning performance acceleration:
  Go deep into the full software/hardware execution stack, including:
  • inference serving and orchestration
  • framework integration layers
  • compiler and graph/runtime support
  • runtime libraries and user‑mode execution paths
  • compute kernel development
  • profiling, benchmarking, and performance tuning
• Model enablement with quality and speed:
  Improve both performance and accuracy for models using popular frameworks, and ensure compatibility with serving ecosystems such as vLLM and SGLang—helping deliver production‑ready inference behavior.
• Hardware/software co‑design and optimization:
  Partner with hardware and platform teams to co‑optimize AI execution for better outcomes:
  • increased throughput
  • reduced latency
  • improved scalability
  • better resource utilization (compute/memory/IO)
  • higher sustained performance under realistic workloads
• Build state‑of‑the‑art AI software components:
  Contribute to the development of software and hardware AI co‑processors/accelerators, delivering reusable libraries, optimized execution paths, and robust integration with existing tooling.
• Cross‑functional collaboration:
  Work closely with cross‑functional teams (compiler/runtime, kernels, platform, and product engineering) to integrate AI capabilities into Ampere’s cloud‑native processor platforms and accelerators.

• Education & experience:
  
  BS in Computer Science, Computer Engineering, Electrical Engineering, or Software Engineering or related technical field & 8 years of related experience; or MS degree & 6 years; or PhD & 3 years.
• Core framework experience:
  Strong experience building with or integrating AI frameworks such as PyTorch, llama.cpp, and ONNX.
• Linux + accelerator/runtime expertise (preferred):
  Experience with developing user‑mode drivers, runtime libraries, or low‑level integration for GPUs or deep learning accelerators in Linux is a plus.
• Strong systems programming & performance skills:
  • Expert in Python and C/C++
  • Strong background in performance profiling and tuning (latency/throughput, memory behavior, kernel efficiency)
• Deep ML understanding:
  Solid understanding of AI/ML concepts including neural networks and data processing frameworks. Experience with modern deep model architectures such as Transformers and Diffusion models is preferred.
• Modern AI tooling fluency (preferred):
  Fluent with modern AI programming tools such as Codex or Claude Code, and comfortable accelerating development workflows.

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