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Position: Machine Learning Engineer Location: On-site in Fremont, CA Duration: 6 Months CTH W2 only. Responsibilities and day-to-day overview are described below. Responsibilities
Translating ambiguous problems into ML solutions by clarifying requirements, designing an approach, and selecting the right algorithms and architectures (e.g., supervised learning, CNNs). Building end-to-end ML pipelines: design, implement, and train models using frameworks like PyTorch and TensorFlow; preprocessing with Pandas; data gathering; cleaning and preprocessing; model training and evaluation; optimization for performance and efficiency; deployment to production environments. Handling complex, multimodal data: work with large and varied datasets including images, multi-spectral sensor outputs, voice, text, and tabular data; develop preprocessing strategies to enable model use. Collaborating with cross-functional teams (production, process, controls, quality) to understand operational pain points and design ML-based solutions that integrate into existing workflows and systems. Deploying, monitoring, and maintaining models: own models post-deployment, set up robust alerting and monitoring to track performance, detect issues, and implement quick fixes when needed. Optimizing algorithms for performance: apply techniques such as quantization, pruning, and TensorRT conversion to meet performance requirements in real-world environments, including embedded or firmware contexts (C++ may be used). Applying strong theoretical foundations: leverage linear algebra, geometry, probability, numerical optimization, and statistics to design models, assess feasibility, and ensure rigorous evaluation. Specializing in high-impact domains: work on problems in computer vision, large language models, recommender systems, or operations research as project needs dictate. Writing high-quality, sustainable code: produce clean, modular, and maintainable code to ensure scalable and updatable ML solutions. Top Requirements (Must Haves)
Algorithm development and optimization: rapid prototyping for high-performance, data-intensive applications; optimization for speed, efficiency, and scalability in production. Programming & integration: Python advanced expertise for data processing, ML model development, and automation; C++ desirable for integration with vehicle firmware and full product lifecycle delivery. Mathematical & statistical foundations: strong background in linear algebra, geometry, probability theory, numerical optimization, and statistics. Deep learning frameworks: hands-on experience with PyTorch and TensorFlow for model development and deployment. Model optimization & deployment: experience with performance-enhancing techniques such as quantization, pruning, and TensorRT conversion; deploying and maintaining production ML use cases. Domain expertise: proficiency in at least one area (Computer Vision, Large Language Models, Recommender Systems, Operations Research). Software engineering best practices: writing clean, sustainable, and modular code; translating research prototypes into production-ready systems. Seniority level
Entry level Employment type
Full-time Job function
Engineering and Information Technology Industries
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Position: Machine Learning Engineer Location: On-site in Fremont, CA Duration: 6 Months CTH W2 only. Responsibilities and day-to-day overview are described below. Responsibilities
Translating ambiguous problems into ML solutions by clarifying requirements, designing an approach, and selecting the right algorithms and architectures (e.g., supervised learning, CNNs). Building end-to-end ML pipelines: design, implement, and train models using frameworks like PyTorch and TensorFlow; preprocessing with Pandas; data gathering; cleaning and preprocessing; model training and evaluation; optimization for performance and efficiency; deployment to production environments. Handling complex, multimodal data: work with large and varied datasets including images, multi-spectral sensor outputs, voice, text, and tabular data; develop preprocessing strategies to enable model use. Collaborating with cross-functional teams (production, process, controls, quality) to understand operational pain points and design ML-based solutions that integrate into existing workflows and systems. Deploying, monitoring, and maintaining models: own models post-deployment, set up robust alerting and monitoring to track performance, detect issues, and implement quick fixes when needed. Optimizing algorithms for performance: apply techniques such as quantization, pruning, and TensorRT conversion to meet performance requirements in real-world environments, including embedded or firmware contexts (C++ may be used). Applying strong theoretical foundations: leverage linear algebra, geometry, probability, numerical optimization, and statistics to design models, assess feasibility, and ensure rigorous evaluation. Specializing in high-impact domains: work on problems in computer vision, large language models, recommender systems, or operations research as project needs dictate. Writing high-quality, sustainable code: produce clean, modular, and maintainable code to ensure scalable and updatable ML solutions. Top Requirements (Must Haves)
Algorithm development and optimization: rapid prototyping for high-performance, data-intensive applications; optimization for speed, efficiency, and scalability in production. Programming & integration: Python advanced expertise for data processing, ML model development, and automation; C++ desirable for integration with vehicle firmware and full product lifecycle delivery. Mathematical & statistical foundations: strong background in linear algebra, geometry, probability theory, numerical optimization, and statistics. Deep learning frameworks: hands-on experience with PyTorch and TensorFlow for model development and deployment. Model optimization & deployment: experience with performance-enhancing techniques such as quantization, pruning, and TensorRT conversion; deploying and maintaining production ML use cases. Domain expertise: proficiency in at least one area (Computer Vision, Large Language Models, Recommender Systems, Operations Research). Software engineering best practices: writing clean, sustainable, and modular code; translating research prototypes into production-ready systems. Seniority level
Entry level Employment type
Full-time Job function
Engineering and Information Technology Industries
Software Development Referrals increase your chances of interviewing at Jobs via Dice by 2x Get notified about new Machine Learning Engineer jobs in Fremont, CA.
#J-18808-Ljbffr