Machine Learning Engineer - NLP & Knowledge Graph

We are working on next-generation tools for document understanding and we need your help building this exciting product!

About INAIT Knowledge

INAIT Knowledge is a growing team that drives innovations related to knowledge graphs and document understanding at INAIT. How do you find the most meaningful information in the ocean of textual data? We build tools that help customers to extract relevant knowledge from millions of unstructured documents in minutes. We develop our own and leverage existing state of the art machine learning techniques to deliver the best possible product on the market. We work on graph machine learning, knowledge engineering, search understanding and retrieval, graph algorithms, and recommendation systems.

Job description and responsibilities

• Use unstructured textual data to build and maintain knowledge graphs in collaboration with the knowledge graph lead.
• Implement, fine-tune, and apply state of the art NLP models and algorithms.
• Apply NLP techniques to construct knowledge graphs.

• As a team player, participate in the design and implementation of NLP-based features in the INAIT platform, powered by knowledge graphs to perform large-scale semantic data integration, search and analysis.

Requirements

Essential skills and experience

• BSc or MSc in engineering (Computer Science, Electrical Engineering, Mathematics, Physics or a related field) or equivalent job-related experience with a minimum of 3 years of hands-on experience in industry.
• Strong software engineering skills. Extensive experience with Python programming language and testing frameworks.
• Good knowledge of core CS concepts, data structures, and algorithms.
• Hands on experience building machine learning models for production in an industry environment.
• Hands on experience with NLP, Data Mining, Knowledge Engineering (entity recognition and linking).

Preferred

• Experience with graph machine learning algorithms (link prediction, knowledge graph completion, knowledge graph embeddings).
• Experience with at least one machine learning library (TensorFlow, PyTorch). Preferably, with PyTorch (Geometric).
• Good understanding of graph theory.
• Experience with distributed systems and scalable data analytics technologies (e. g. Apache Spark).
• Experience with graph databases, triple stores, and graph query languages (e. g. Neo4J, Stardog, Cypher, SPARQL, Gremlin).
• Experience with graph visualization technologies and tools (e. g. SigmaJS, Gephi, Neo4J Bloom).

Profile

• Excellent interpersonal and communication skills, including written and spoken English.
• Experience working in a fast-paced collaborative, multi-cultural, and dynamic environment.
• Have the ability to take on complex problems, learn quickly, iterate, and persist towards a good solution.
• Thrive on working together with other experts in their respective fields. Keen to share knowledge and learn from other team members.
• Proven ability to work both independently, and in team-based environments.

Where: Lausanne, Switzerland

Start date: As soon as possible

Activity rate: 100%

Duration of contract: CDI

Competitive salary & benefits

Cutting edge research, Positive work environment, Hybrid work model (combination of WFH and work from the office),....