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Job Summary
We are looking for a hands-on AI Optimization Engineer who thrives at the intersection of algorithms systems engineering and production-grade software. We are looking for someone who wants to work at the heart of state-of-the-art AI not yesterdays models but the frontier: Generative AI Transformers Vision-Language Models (VLMs) and the emerging wave of Agentic AI systems.
In this role you will contribute to the engineering of the core optimization technology that makes these advanced models run efficiently on NXPs next-generation edge platforms such as Ara 2. While Neural Network Quantization will be your primary focus your impact will go far beyond it. You will architect high-performance software and collaborate across compiler and hardware teams. Your work will directly define how frontier AI models are executed on-device enabling fast reliable and power-efficient GenAI on resource-constrained platforms.
If you want to work where AI innovation meets real-world engineering and build the infrastructure that defines what tomorrows smart autonomous AI-powered devices can do this is the role for you.
Job Responsibilities
1. Optimization Tools: Own and evolve our production-grade optimization tools. You will design and implement quantization features including mixed-precision flows that are robust enough for global deployment.
2. Pipelines: Design and maintain scalable PTQ (Post-Training Quantization) and QAT (Quantization-Aware Training) workflows ensuring seamless integration with other optimizations and downstream deployment stacks.
3. Applied Innovation & Tooling: Collaborate on ProofofConcepts (POCs) for state-of-the-art quantization techniques. You wont just prototype; you will evaluate the real-world impact of novel ideas and engineer the bridge that turns successful experiments into production-ready deployment recipes and developer tooling.
4. Numerical & Hardware Rigor: Apply your math foundation to implement approximation algorithms (range estimation bias correction BN-folding) while ensuring bit-exactness on target hardware. You will bridge the gap between abstract math and physical constraints like accumulator widths saturation and rounding behaviors.
5. Systems Performance: Profile and optimize the hot paths of our optimization toolchain to meet strict memory and compute-constrained targets.
6. Cross-Functional Leadership: Act as the technical bridge between AI Research and Hardware Engineering providing quantified guidance on how choices impact model accuracy and performance.
7. Deployment Architecture: Document algorithmic tradeoffs and derive gold-standard deployment recipes acting as technical mentor for other engineering teams ensuring deployment strategies are scalable and repeatable.
Job Qualifications
Required Background
Education: MSc or Ph.D. (is a plus) in Computer Science Electrical Engineering or Mathematics with a specialization in Machine Learning or Deep Learning.
Systems Programming: Mastery of Python and C/C. You should be comfortable with memory management and understanding how code maps to hardware (CPUs/NPUs).
AI Expertise: Proven experience in AI/ML with a good understanding of CNN architectures and Generative AI (Transformers).
AI Optimization: Experience with (or a strong desire to learn) quantization workflows and troubleshooting accuracy regressions.
Technical Stack: Strong hands-on experience with PyTorch ONNX and other AI/ML frameworks.
Preferred
Hardware Acceleration: Experience with hardware accelerators device-level profiling and diagnosing memory bottlenecks.
Embedded Mindset: Familiarity with the constraints of embedded systems (latency power memory bandwidth).
Advanced AI: Experience implementing state-of-the-art quantization for generative AI (e.g. GPTQ Smoothquant etc).
Compilers: Knowledge of MLIR or TVM is a significant plus.
What You Will Gain
Be part of a pioneering team shaping the future of AI and edge computing.
Work on innovative projects that solve real-world challenges.
Opportunity to grow with a dynamic forward-thinking company.
Competitive salary benefits and a collaborative work environment.
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