14 часов назад
Staff Software Engineer (AI)
166 000 - 188 000$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Staff Software Engineer (AI SoCs): Developing and optimizing low-level software, microcode, and runtimes for deploying AI workloads on Ambarella AI SoCs with an accent on heterogeneous compute orchestration, memory hierarchy utilization, and system-level debugging. Focus on building deployment and profiling tools, optimizing performance across accelerator architectures, and integrating computer vision and deep learning workloads with hardware platforms.
Location: US Headquarters
Base salary: $166,000–$188,000 annually for Level 3; $188,000–$212,000 annually for Level 4.
Company
develops AI vision processors and software solutions for extracting data from high-resolution video streams.
What you will do
- Develop and debug microcode, runtime components, and system-level software for deploying AI workloads on AI SoCs.
- Optimize heterogeneous compute cores and multi-level memory hierarchies for performance.
- Build tooling for workload deployment, profiling, debugging, and performance analysis.
- Enhance SDKs and workflows for model and algorithm deployment to AI platforms.
- Collaborate with architecture, hardware, compiler, and application teams on production-ready solutions.
- Help customers optimize and integrate models onto SoCs.
Requirements
- Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or an equivalent field.
- 5–10 years of experience in systems software, embedded software, or firmware engineering.
- Strong C, Python, and low-level programming experience.
- Deep understanding of computer architecture and microprocessor fundamentals.
- Experience with modern software development processes and system-level debugging.
- Strong analytical, communication, interpersonal, and planning skills.
Nice to have
- Experience with heterogeneous compute-core SoCs and accelerators.
- Knowledge of runtimes, operating systems, or RTOS internals.
- Knowledge of deep learning, computer vision, and AI workloads such as CNNs, LLMs, or VLMs.
Culture & Benefits
- Hands-on work at the hardware–software boundary.
- Focus on challenging performance optimization problems.
- Work in a fast-moving, product-driven environment with complex technical challenges.
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