Harman is hiring for Software Engineer

HARMAN (harman.com) designs and engineers connected products and solutions for automakers, consumers, and enterprises worldwide, including connected car systems, audio and visual products, enterprise automation solutions; and services supporting the Internet of Things. With leading brands including AKG®, Harman Kardon®, Infinity®, JBL®, Lexicon®, Mark Levinson® and Revel®, HARMAN is admired by audiophiles, musicians and the entertainment venues where they perform around the world. More than 50 million automobiles on the road today are equipped with HARMAN audio and connected car systems. Our software services power billions of mobile devices and systems that are connected, integrated and secure across all platforms, from work and home to car and mobile. HARMAN has a workforce of approximately 30,000 people across the Americas, Europe, and Asia. In March 2017, HARMAN became a wholly-owned subsidiary of Samsung Electronics Co., Ltd. HARMAN is an Equal Opportunity, Affirmative Action employer. Minorities, women, veterans and individuals with disabilities are encouraged to apply. HARMAN offers a great work environment, brilliant career opportunities, professional training and competitive compensation. Looking for a challenge where your experience is valued? Come see what you can achieve as a leader with HARMAN! 

HARMAN is an Equal Opportunity /Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or Protected Veterans status. HARMAN offers a great work environment, challenging career opportunities, professional training and competitive compensation. (www.harman.com)

• Assist in the design, development, and implementation of machine learning models and algorithms.
• Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.
• Preprocess and analyze large datasets to extract meaningful insights and patterns.
• Perform data cleaning, feature engineering, and data augmentation to improve model performance.
• Conduct experiments to test hypotheses and evaluate the performance of different machine learning approaches.
• Implement and optimize machine learning pipelines for efficiency and scalability.
• Deploy machine learning models into production environments and monitor their performance.
• Document methodologies, processes, and results to ensure reproducibility and knowledge sharing.
• Stay up to date with the latest advancements in machine learning and related technologies.