Sr. Staff Edge AI Applied Machine Learning Engineer

Company Overview Ambiq is on a mission to enable intelligence everywhere — powering the AI edge revolution with the world's lowest-power semiconductor solutions. Built on our proprietary sub- and near-threshold technology, our chips deliver multi-fold improvements in energy efficiency without costly process scaling. Since 2010, we've shipped over 300 million units to customers building smarter wearables, medical devices, IoT products, and AI-powered edge applications. Our cross-functional teams span design, research, development, production, marketing, sales, and operations across Austin, Hsinchu, Shanghai, Shenzhen, and Singapore. We move fast, tackle hard problems, and create space for people to grow through complex, meaningful work that shapes the future of technology. We're looking for self-motivated, creative problem-solvers who are eager to push technological limits and make a real impact in energy efficiency. At Ambiq, we live by five values: Innovate. Collaborate. Focus. Learn. Achieve. If that's you, join us — the intelligence everywhere revolution starts here. Scope Ambiq is seeking an experienced Edge AI Applied ML Engineer with deep experience in audio and computer vision. In this role, you will design, train, optimize, and deploy highly efficient on-device AI models—from ultra-small (tens of KB) to larger (hundreds of MB) footprints—targeting resource-constrained, real-time, battery-powered devices. While the cloud has been the default home for AI, the next frontier is distributing intelligence everywhere—directly onto real-world devices. Edge AI enables real-time responsiveness, stronger privacy, lower bandwidth cost, and reliable operation even without connectivity. This role will help accelerate the shift to on-device intelligence across a rapidly growing ecosystem of health and fitness wearables, smart glasses, industrial IoT, and always-on sensors. You’ll also help evolve our award-winning open-source AI Development Kits (ADKs): modular tooling that enables developers to mix-and-match datasets, model architectures, tasks, training recipes, and deployment targets. You will bridge cutting-edge research and practical productization by building production-grade demos, reference applications, and customer-facing tooling that accelerates real-world adoption. Responsibilities Develop and optimize on-device ML models for constrained, real-time, battery-powered products, balancing accuracy with latency, memory, and energy. Build and maintain Ambiq’s open-source ADKs for modular datasets, models, tasks, and training recipes. Translate cutting-edge research into production-grade demos and reference implementations. Apply model efficiency techniques: quantization, compression, pruning, and structured sparsification. Serve as a domain expert in audio and vision (data strategy, evaluation, and failure analysis). Port and optimize customer models to Ambiq edge runtimes, ensuring correctness, performance, and usability. Deliver and promote customer-ready assets: docs, tutorials, examples, benchmarks, plus white papers and conference representation. Qualifications BS in Computer Science or related field + 5+ years of relevant experience (or equivalent practical experience). MS or PhD in related disciplines (ML, EE, signal processing, computer vision, robotics) is highly desirable.Strong proficiency in Python; working proficiency in C/C++ and/or Rust for performance and runtime integration. Domain expertise in audio (KWS, speech enhancement, SLM, TTS) and/or vision (classify/detect/segment/pose/OBB/track), with DSP fundamentals (e.g., FFT). Comfortable in Linux development with Docker/dev containers (able to work across Mac/Windows as needed). Experience with one or more training frameworks: PyTorch, TensorFlow, JAX, Keras. Strong ML engineering fundamentals: data pipelines, augmentation, metrics, experiment reproducibility, and failure analysis. Familiarity with edge deployment stacks such as ONNX, LiteRT, ExecuTorch. Hands-on with edge optimization: quantization (PTQ/QAT), compression, and (structured) sparsification, plus profiling for latency/memory/energy tradeoffs. Efficient use of AI-assisted development tools while maintaining rigor (testing, review, reproducibility). Must be currently authorized to work in the United States for any employer. We do not sponsor or take over sponsorship of employment visas (now or in the future) for this role.

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