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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Neuronetwork System Engineer - **Company:** TETRAMEM INC - **Location:** San Jose, CA, United States - **Experience:** Expert - **Salary:** $110,000.0 - $250,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Applicant Tracking Systems, Audio Signal Processing, C++ (Programming Language), Field-Programmable Gate Array (FPGA), Python (Programming Language), Machine Learning, Tensorflow, Systems Architecture, Application Specific Integrated Circuits, Pytorch, Information Technology, Low Latency, ONNX (Open Neural Network Exchange) Format, Hardware Acceleration, TensorRT - **Published:** August 25, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=3792ff96831cb169 ## About the Role * 5+ years of experience or PhD in Computer Science, Electrical Engineering, or related fields. * Strong experience in machine learning, with a focus on edge AI and lightweight model deployment. * Expertise in ML frameworks such as PyTorch, TensorFlow, JAX. * Proficiency in programming languages such as C/C++, Python, and experience with ML model optimization. * Ability to work independently and collaboratively in a fast-paced startup environment. * Ability to provide mentorship, technical guidance, and career development support to junior engineers and interns. Experience in one or more of the following areas considered a strong plus: * Understanding of ML compiler and runtime design. * Experience working with tools such as Optimum, ONNX, TensorRT, TFLite/LiteRT, ncnn, or CoreML. * Familiarity with hardware acceleration techniques. * Experience in embedded system development. ## Description * Develop, optimize, and deploy lightweight machine learning models for edge AI applications, particularly for audio processing. * Implement and optimize ML models on embedded platforms, including FPGA and custom ASIC solutions. * Work closely with hardware and software teams to integrate ML models into production systems. * Research and implement state-of-the-art ML techniques to enhance model efficiency, latency, and power consumption for embedded AI applications. * Improve inference efficiency and model compression techniques, including quantization, pruning, and knowledge distillation. * Collaborate with cross-functional teams to drive innovation and contribute to the overall system architecture. * Provide technical leadership and mentorship to junior engineers. * Publish research findings, present at conferences, and contribute to open-source projects when applicable., To ensure a fair, consistent, and efficient hiring process, all candidates must apply through TetraMem's official ClearCompany Applicant Tracking System (ATS). Applications submitted through the ATS allow our hiring team to evaluate candidates using a standardized process and ensure timely communication throughout the recruitment process. To promote equal consideration for all applicants, applications submitted outside of the ClearCompany ATS, including direct emails, LinkedIn messages, or unsolicited submissions to employees, may not be reviewed or considered. ## Related Videos - [Making neural networks portable with ONNX](https://www.wearedevelopers.com/videos/301-making-neural-networks-portable-with-onnx) - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Trends, Challenges and Best Practices for AI at the Edge](https://www.wearedevelopers.com/videos/630-trends-challenges-and-best-practices-for-ai-at-the-edge) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) ## Related Articles - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)