Machine Learning Engineer

🔷 Machine Learning Engineer | EV & Automotive Manufacturing 🔷 Industry: Electric Vehicle (EV) & Automotive Manufacturing Employment Type: Contract Work Model: Onsite in Fremont, CA(Need only locals) Start: ASAP 🚀 Why This Role Stands Out We are partnering with a leading EV & Automotive Manufacturing organization on a large-scale AI/ML-driven digital transformation. This role is for a hands-on Machine Learning Engineer who can design, build, and optimize scalable machine learning solutions in a high-volume, complex manufacturing environment. If you enjoy working closely with manufacturing operations, engineering, data, and product teams to deploy ML models that drive real-world impact—such as predictive maintenance, quality optimization, and demand forecasting—this is a high-impact opportunity. Serve as a member of the factory software machine learning and computer vision team. Design, develop and implement critical machine learning models that operate on our factory and warehouse environments. Take ambiguous problem statements and build end-to-end models using a variety of techniques and tools like supervised learning, convolutional neural networks, Pytorch, Pandas, etc. Work closely with partners in production, process, controls, quality, etc. to build solutions for the hardest problems faced in our operations. Will take ownership of models in production and ensure there is proper alerting systems in place to rapidly deal with any issues that occur. Must be able to clean and deal with diverse and heterogeneous data sets that combine many modalities like images, multi-spectral sensor outputs, voice, text, and tabular data. Minimum Requirements: In-depth knowledge of Python for high-performance data-intensive applications. Familiarity with at least one modern deep learning framework (Pytorch, Jax, Tensorflow, etc.) Expertise in one or more areas like computer vision, large language models, recommender systems, or operations research. Foundational knowledge of statistics for comparing models and assessing solution feasibility and performance. Real-world experience deploying and maintaining production machine learning use-cases. Passion for clean code and sustainable modular code for taking research ideas into production.