Lab Summary:

Samsung Knox Cloud Service team focuses on the development and research of large-scale production-level machine learning algorithms, models, and systems and aims at representing Samsung's leadership in large-scale machine learning products. The team productizes a wide range of Machine Learning algorithm from various Samsung Research teams and Research papers including both device and server applications. This is an exciting and unique opportunity for a talented and hard-working machine learning engineer to get involved in envisioning, designing and implementing cutting-edge products with a growing team.

Position Summary:

In this position, you will join a collaborative team of world-class developers/research engineers and have a chance to integrate your innovative solutions into our Security & Intelligence services.

Come join the Samsung Knox Cloud Service team and help us shape the future role of Samsung in the machine learning domain!

Position Responsibilities:

• Design, develop, and productize both device and server-side machine learning solutions for Samsung services
• Productize machine learning algorithms from Samsung Research teams or Research Papers
• Create quick prototypes and proof-of-concepts
• Design experiments, perform evaluations, and apply enhancements to our products
• Create state of the art ML based security solutions for Samsung devices

Required Skills:

• Master’s or Ph.D. degree in Computer Science, Statistics, Math, Physics, Mechanical Engineering, or other quantitative fields or equivalent combination of education, training and experience
• 3+ years’ experience in building machine learning and deep learning-based software solutions
• Solid theoretical background in deep/machine learning and exceptional hands-on experiences in changing the model source code for optimal performance
• Hands-on experience with various MLs, such as GNN, VAEs and transformer-based models
• Understanding of GenAI Foundation Models, Vector DB and Graph DB: Leveraging foundational AI models and vector/graph database technologies for advanced AI capabilities
• Familiar with RAG (Retrieval-Augmented Generation) and model fine tuning: Employing RAG techniques for enhanced AI responses and fine-tuning embedder models for optimal performance
• Familiar with various quantization techniques including QAT and PTQ
• Use of Orchestration Tools: Utilizing advanced tools like Langchain, Pydantic and others for efficient AI model management
• Strong programming skills with Python
• Exceptional problem-solving and interpersonal skills and proven ability to excel in a fast-paced development team
• Familiar with Amazon Web Services or Google Cloud Platform
• Familiar with big data tools, data pipelines, RDBMS and NoSQL databases
• Experience with security & privacy concepts such as zero trust and differential privacy

Additional Information

Be careful not to disclose information related to the trade secrets of your previous or current employer(s)

Essential Job Functions

This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, and frequently operate standard office equipment, such as telephones and computers.

Samsung Research America is committed to complying with all Federal, State and local laws related to the employment of qualified individuals with disabilities. If you are an individual with a disability and would like to request a reasonable accommodation as part of the employment selection process, please contact the recruiter or email sratalent@samsung.com.

Affirmative Action / Equal Opportunity

Samsung Research America is an Affirmative Action and Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability, or status as a protected veteran.

For more information regarding protection from discrimination under Federal law for applicants and employees, please refer to the links below.

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