Lab Summary

Bixby is an intelligent personal assistant which is only available as a built-in application on Samsung flagship devices and wearables. This application uses Natural Language Processing and Knowledge-Based AI to perform tasks on these devices using multimodal inputs and additional contextual information, including but not limited to making phone calls, sending text messages, setting up meetings, opening apps, setting alarms and timers, getting directions, answering general questions, providing information about restaurants and other businesses, etc.

The Natural Language Processing and Knowledge-Based AI team aims to create a delightful experience for Bixby customers by making Bixby understand the intent behind any type of request quickly and accurately, and to proactively adapt to the users’ needs. You will collaborate closely with experts in Machine Learning, Natural Language Processing and Knowledge-Based AI, and contribute to advancing the state of the art in virtual assistants.

Position Summary:

As a Senior Knowledge-Based AI Engineer you will primarily focus on building the Natural Language Understanding and Proactive Behavior platform for Bixby by working with Product Managers / Subject Matter Experts, Lab Leaders, Linguistic Experts, brainstorm different ideas, research, build POCs and propose solutions that cater to the broader business needs. You will work with a small and nimble team to help design machine learning models, data pipelines, integrate into and maintain production systems and analyze key metrics for decision makers to provide insights that will be beneficial to Bixby consumers.

Responsibilities:

• As a core member of the NLP and KBAI team, you will research, prototype, develop, deploy and scale innovative ML/DL solutions in collaboration with Linguistic Experts and Product Management teams
• You will develop predictive models on large-scale datasets to address various business problems leveraging advanced statistical modeling, machine learning, or data mining techniques
• Design and implement infrastructure for orchestrating end to end machine learning lifecycles
• Set up processes to monitor and continually improve efficiency and performance of models
• Software development including algorithm implementation, optimization, performance profiling, integration to production systems, testing and documentation
• Write high-quality production code as you build and maintain robust, scalable machine learning systems
• Program primarily in Python and / or Java using efficient algorithms and software design patterns
• Scale and improve performance of NLP and KBAI systems in production

Required Qualifications:

• MS/PhD in KBAI, ML, AI, Engineering or equivalent
• 7+ years of relevant experience in Data Mining, Knowledge Extraction, Knowledge Representation Learning, Graph embeddings, Graph-Based NLP, Ontology Learning, Ontology Alignment, Recommender Systems
• 10+ years of experience with building end-to-end systems based on knowledge bases and machine learning or deep learning methods (ETL, modeling and deployment)
• Strong understanding of computer science fundamentals such as algorithms, data structures and run-time analysis
• Proficiency in Java and Python
• Experience with Knowledge Discovery algorithms such as Association Rule Learning, Association Generation Tree (AGT), Frequent Pattern Growth (FP Growth), Apriori, etc.
• Experience with Recommender System approaches such as Memory-Based Collaborative Filtering (e.g., User-Based algorithm), Model-Based Collaborative Filtering (e.g., Matrix Factorization), Similarity Search (e.g., k-Nearest Neighbors, Pearson Correlation), cold start solutions (e.g., Multi-Armed Bandit algorithm), Content-Based Filtering (e.g., Bayesian Classifiers, Cluster Analysis, Decision Trees, Deep Learning)
• Experience with deep learning frameworks like TensorFlow, PyTorch, JAX and libraries like Hugging Face transformers, Deep Graph Library, DGL-KE, etc.
• Experience with graph deep learning architectures such as Transformers, Tree-LSTMs, Graph Neural Networks, etc.
• Experience with cutting-edge Graph Embedding models such as tensor decomposition models (TuckER), geometric models (CrossE, RotatE) and deep learning models (ConvR, RSN)
• Experience with ontology learning, population and alignment algorithms
• Experience with graph-based tasks such as Link Prediction, Triple Classification, Entity Recognition, Clustering, and Relation Extraction