Machine Learning Engineer

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Spotter Labs

WebsiteLinkedInAll Job Openings

1 - 10 employees

Founded 2020

📦 Logistics

🚗 Transport

🤖 Artificial Intelligence

💰 Seed Round on 2021-11

Logistics • Transport • Artificial Intelligence

Spotter Labs is a company that offers AI-driven solutions to enhance trucking efficiency and maximize earnings for drivers and owner-operators. Utilizing advanced technology, Spotter enables users to schedule personal work times, select preferred destinations, and avoid unfavorable markets using a NO-GO map. The platform intelligently recommends loads based on their score, connecting drivers with optimal opportunities. With special considerations for motor carriers (MCs) older than six months, Spotter provides competitive pricing and services for various stages of MC age.

📋 Description

• Develop, train, and evaluate machine learning models. • Prepare and analyze datasets for model training. • Improve model performance through experimentation and testing. • Deploy and maintain ML models in production environments. • Collaborate with software engineers to integrate ML solutions into products. • Monitor model performance and continuously improve accuracy. • Stay up to date with new machine learning techniques and technologies.

🎯 Requirements

• 1+ year of experience in machine learning, software engineering, data science, or a related technical role or strong personal projects demonstrating ML skills • Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field is a plus, but not required. • Basic understanding of machine learning concepts and algorithms. • Experience with Python. • Familiarity with machine learning libraries such as PyTorch, TensorFlow, or scikit-learn. • Comfortable working with data and writing clean, maintainable code. • Familiarity with Git. • Good problem-solving skills. • Strong written and verbal English communication.

🏖️ Benefits

• Fully remote work • Flexible working environment • Opportunity to work on real AI products • Challenging technical problems with room to grow • Collaborative and fast-moving team • Competitive compensation based on experience