> Markdown version of [/jobs/ext/2833468-senior-staff-machine-learning-engineer-in-san-jose](https://www.wearedevelopers.com/jobs/ext/2833468-senior-staff-machine-learning-engineer-in-san-jose). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Staff Machine Learning Engineer in San Jose - **Company:** Energy Jobline - **Location:** San Jose, CA, United States - **Experience:** Expert - **Salary:** $227,000.0 - $300,000.0 - **Contract:** Permanent contract - **Skills:** Adobe Flash, Artificial Intelligence, Computer Vision, C++ (Programming Language), CAN Bus, Program Optimization, Software Debugging, Linux, Python (Programming Language), Machine Learning, Message Queuing Telemetry Transport (MQTT), Natural Language Processing, Tensorflow, Software Engineering, Supervised Learning, Pytorch, Large Language Models, Data Strategy, Scikit Learn, Information Technology, Statistics Packages, ONNX (Open Neural Network Exchange) Format, TensorRT, Multiaccess Edge Computing, C++14, Recurrent Neural Networks - **Published:** September 10, 2026 - **Apply:** https://www.energyjobline.com/job/senior-staff-machine-learning-engineer-san-jose-31578273 ## About the Role * Bachelor's degree in Computer Science, Electrical Engineering, Software Engineering, or a related field. * 10+ years in Machine Learning Engineering, with 3+ years focused on Edge AI or Embedded Systems. * Proven experience mentoring junior engineers in software development. * Expert Python (for training) and decent working knowledge of modern C++ (C++14/17 for inference). * Deep proficiency with PyTorch or TensorFlow, and experience with inference engines like ONNX, TFLite, or TVM. * Experience with NLP techniques for textual data parsing, sequence modeling (RNN/GRU), vector stores, or lightweight LLMs/SLMs. * Experience with libraries like scikit-learn, tslearn, or statsmodels for anomaly detection on sensor data. * Proven ability to lead technical projects from concept to production in an ambiguous, fast-paced environment. Ability to communicate with stakeholders and articulate trade-offs. * Experience deploying to Edge environments (e.g., ARM-based), managing memory manually, and working with limited compute resources. * Candidates with a strong Computer Vision (CV) / ADAS track record are highly encouraged to apply! Desired Skills: * MS/PhD in Computer Science, Engineering, or related fields. * Familiarity with Edge systems and preferably automotive formats (CAN, DBC, UDS, SOME/IP, or MQTT. * Understanding of Linux/QNX kernel logs (dmesg), process states, and OS-level debugging. * Experience with NVIDIA TensorRT, Qualcomm SNPE. ## Description * Build and train AI Edge models (e.g., Transformers, LLMs, CNN, LSTM, Trees) to process unstructured application logs, kernel traces, and multi-modalities. * Integrate ML flows, including cloud-based LLM APIs (Gemini, OpenAI, Claude), with emphasis on synthetic data creation. * Develop algorithms to automatically cluster log patterns and detect software regressions, conditions, or crash precursors. * Design unsupervised and supervised learning models (e.g., Autoencoders, Isolation Forests) to monitor time-series data from CAN bus and on-board sensors. * Implement logic to correlate signal anomalies (e.g., ADAS drifts, sensor spikes, latency jitters) across different modalities with system events to identify root causes. * Port and optimize PyTorch/TensorFlow models into production-grade models for execution on CPU/GPU-bound targets or embedded NPUs. * Apply quantization, pruning, distillation, and memory optimization to ensure models run within strict RAM/Flash budgets. * Define the data strategy for on-device filtering: pre-processing on device and decide which data is processed locally versus processed in the cloud. * Lead the architecture for the edge ML pipeline and mentor junior engineers on best practices for embedded AI. ## Related Videos - [A Hitchhikers Guide to Container Security - Automotive Edition 2024](https://www.wearedevelopers.com/videos/1119-a-hitchhikers-guide-to-container-security-automotive-edition-2024) - [Intelligent Data Selection for Continual Learning of AI Functions](https://www.wearedevelopers.com/videos/367-intelligent-data-selection-for-continual-learning-of-ai-functions) - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [How Machine Learning is turning the Automotive Industry upside down](https://www.wearedevelopers.com/videos/61-how-machine-learning-is-turning-the-automotive-industry-upside-down) - [Docker exec without Docker](https://www.wearedevelopers.com/videos/1094-docker-exec-without-docker) - [Developer Tools for Microsoft Azure](https://www.wearedevelopers.com/videos/450-developer-tools-for-microsoft-azure) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How software is steering vehicle technology](https://www.wearedevelopers.com/magazine/515-how-software-is-steering-vehicle-technology) - [6 Emerging Technologies We’ll Learn About in 2025](https://www.wearedevelopers.com/magazine/381-6-emerging-technologies-we-ll-learn-about-in-2025) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)