The role involves designing knowledge extraction pipelines and representation schemas, developing semantic parsing techniques, and collaborating with AI researchers to enhance knowledge systems.
About Us
AlphaPoint’s AI Labs’ team of engineers and AI scientists is solving complex business problems by bridging the gap between transformative breakthroughs in AI technology and increasingly competitive markets.
Our team is developing and applying the latest generative AI, data and knowledge modeling technologies to large scale problems, right at the edge of what is possible.
AlphaPoint is a financial technology company powering digital asset exchanges and brokerages worldwide.
The Role
AlphaPoint’s AI Labs’ team of engineers and AI scientists is solving complex business problems by bridging the gap between transformative breakthroughs in AI technology and increasingly competitive markets.
Our team is developing and applying the latest generative AI, data and knowledge modeling technologies to large scale problems, right at the edge of what is possible.
AlphaPoint is a financial technology company powering digital asset exchanges and brokerages worldwide.
The Role
- Design and implement knowledge extraction pipelines from diverse unstructured and semi-structured data sources.
- Design and develop knowledge representation schemas and ontologies to model complex domain knowledge.
- Develop and optimize semantic parsing techniques to convert natural language into structured queries or representations.
- Utilize and contribute to graph query languages for efficient retrieval.
- Use query languages to discover deep connections between entities, predict node attributes, perform clustering techniques and anomaly detection methods.
- Collaborate with AI researchers, data scientists, and software engineers to integrate knowledge systems into larger platforms.
- Evaluate and select appropriate tools and technologies and stay up-to-date with the latest advancements for knowledge representation and reasoning.
- Contribute to the continuous improvement of our knowledge engineering processes and best practices.
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Linguistics, or a related field.
- Proficiency in graph databases (e.g., Neo4j, ArangoDB, Amazon Neptune) and graph query languages (e.g., Cypher, SPARQL, Gremlin).
- Familiarity with knowledge extraction methods, including information extraction, entity recognition, and relation extraction
- Proven experience in using Language Models (LMs) in knowledge retrieval and knowledge extractions applications, including the use of fine tuning, RAG architectures, query engineering and semantic parsing against knowledge graphs.
- Excellent programming skills in Python, Node.js, and familiarity with ML libraries such as PyTorch and Tensorflow
- Strong understanding of ontology design principles; experience with languages like OWL or RDF is a plus.
- Ability to work independently and as part of a collaborative team.
- Excellent problem-solving and communication skills.
- PhD in a relevant field.
- Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) applied to knowledge representation tasks.
- Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and their AI/ML services.
- Experience in a production environment with large-scale knowledge systems.
- 100% Remote Work Environment
- Competitive compensation
- Equity or stock options (if applicable)
- A culture of autonomy, experimentation, and learning
- Opportunity to make a real impact on company trajectory
Top Skills
Amazon Neptune
Arangodb
Cypher
Gremlin
Neo4J
Node.js
Owl
Python
PyTorch
Rdf
Sparql
TensorFlow
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