Job Description

Contact Details:

1.Sandeep Bisane
Email: sandeep.bisane@peer-consulting.com
Cell: (732) 802-7361

Job Title: Machine Learning Engineer

Location: Fremont, CA

Duration: 12 Months+

Years of Experience: 5+ Yrs.

Required Hours/Week: 40hrs./Week

Job Description:

• The end-client is hiring a Machine Learning Engineer for their software machine learning and computer vision team to design, develop, and implement critical machine learning models supporting factory and warehouse operations.
• You will transform ambiguous problem statements into robust end-to-end solutions using a variety of machine learning techniques and tools, including supervised learning, convolutional neural networks, and modern frameworks such as PyTorch and Pandas.
• You will collaborate closely with partners in production, process, controls, and quality to deliver solutions for the most challenging problems in our operations.
• Your work will involve evaluating and deploying models in production environments, ensuring rapid and reliable alerting systems, and addressing operational issues as they arise.
• You must be adept at handling diverse, heterogeneous datasets that span multiple modalities, including images, multi-spectral sensor outputs, voice, text, and tabular data.

Responsibilities:

• Design, develop, and deploy machine learning models for factory and warehouse environments.
• Collaborate with cross-functional teams to identify, define, and solve high-impact operational challenges.
• Build and maintain end-to-end machine learning pipelines, from data collection and preprocessing to model deployment and monitoring.
• Evaluate and compare models using statistical methods to ensure optimal performance and feasibility.
• Ensure robust alerting and monitoring systems are in place for deployed models to address issues rapidly.
• Work with diverse datasets, integrating multiple data types such as images, sensor data, voice, text, and tabular information.
• Write clean, modular, and sustainable code to translate research ideas into production-ready solutions.

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