Machine Learning Engineer

Position : Machine Learning Engineer Experience : 9+yrs Visa : GC, USC, GCEAD, H4EAD, TN Tax Term : W2 Client : Tesla Location : Fremont, CA, onsite Project Description Design, develop and implement critical machine learning models that operate on our factory and warehouse environments Duties/Day to Day Overview 1. Translating Ambiguous Problems into ML Solutions You will take loosely defined or complex business and operational problems and determine how to solve them using machine learning. This involves clarifying requirements, designing an approach, and selecting the right algorithms and architectures (e.g., supervised learning, CNNs). 1. Building End-to-End Machine Learning Pipelines You will design, implement, and train ML models using frameworks like PyTorch and TensorFlow, leveraging data tools like Pandas for preprocessing and analysis. The process will include: • Data gathering • Cleaning and preprocessing • Model training and evaluation • Optimization for performance and efficiency • Deployment to production environments 1. Handling Complex, Multimodal Data You will work with large and varied datasets — including images, multi-spectral sensor outputs, voice, text, and tabular data — and develop preprocessing strategies to make this data usable for machine learning models. 1. Collaborating with Cross-Functional Teams You will partner with production, process, controls, and quality teams to understand operational pain points and design ML-based solutions that integrate seamlessly into existing workflows and systems. 1. Deploying, Monitoring, and Maintaining Models You will own models after deployment, setting up robust alerting and monitoring systems to track performance, detect issues, and initiate quick fixes when needed. 1. Optimizing Algorithms for Performance You will improve speed and efficiency through quantization, pruning, and TensorRT conversion, ensuring that models meet performance requirements in real-world environments — including embedded or firmware-integrated contexts (leveraging C++ if needed). 1. Applying Strong Theoretical Foundations You will use expertise in linear algebra, geometry, probability theory, numerical optimization, and statistics to design models, assess feasibility, and ensure rigorous evaluation. 1. Specializing in High-Impact Domains Depending on the project, you may work on problems in computer vision, large language models, recommender systems, or operations research, applying domain-specific techniques to deliver maximum value. 1. Writing High-Quality, Sustainable Code You will produce clean, modular, and maintainable code to ensure that ML solutions are scalable and easy to update, supporting long-term sustainability of deployed systems. Top Requirements (Must haves) Algorithm Development & Optimization • Rapid prototyping of algorithms for high-performance, data-intensive applications. • Optimization for speed, efficiency, and scalability in production environments. 1. Programming & Integration • Python – advanced expertise for data processing, ML model development, and automation. • C++ – desirable proficiency for integration with vehicle firmware and full product lifecycle delivery. 1. Mathematical & Statistical Foundations • Strong background in: • Linear Algebra and Geometry – essential for ML, graphics, and computer vision. • Probability Theory – for modeling uncertainty and decision-making. • Numerical Optimization – for training and refining models. • Statistics – for model evaluation and performance analysis. 1. Deep Learning Frameworks • Hands-on experience with PyTorch and TensorFlow for model development and deployment. 1. Model Optimization & Deployment • Skilled in performance-enhancing techniques: • Quantization • Pruning • TensorRT conversion • Deploying and maintaining production machine learning use cases. 1. Domain Expertise • Proficiency in at least one specialized area: • Computer Vision • Large Language Models (LLMs) • Recommender Systems • Operations Research 1. Software Engineering Best Practices • Writing clean, sustainable, and modular code. • Translating research prototypes into robust, production-ready systems.