Research Engineer - Neuro-Symbolic AI & Multimodal Assistant Systems

Job Description

Roles & Responsibilities:

• Conduct cutting-edge research in Neuro-Symbolic AI, focusing on the integration of formal knowledge representations (e.g., ontologies, knowledge graphs, logic-based systems) with modern machine learning and deep learning techniques.

• Design, develop, and optimize multimodal data pipelines that combine text, vision, audio, sensor, and structured data to support knowledge-driven Neuro-Symbolic AI systems.

• Architect and implement hybrid reasoning and analytical engines capable of complex problem-solving, including causal reasoning, planning, explainable decision-making, and symbolic-neural inference.

• Design and develop knowledge-driven AI systems with natural language interaction capabilities, enabling explainable, trustworthy, and human-centered AI assistants.

• Integrate Neuro-Symbolic reasoning with large language models (LLMs) and multimodal foundation models using knowledge grounding, structured retrieval and reasoning-aware workflows to improve robustness, interpretability, and domain adaptability.

• Design, build, and evolve semantic assets - such as ontologies, knowledge graphs, semantic data models, and symbolic rules / constraints - that ground, validate, and explain neuro-symbolic AI systems in production-oriented research settings.

• Prototype, evaluate, and validate research concepts through experiments, benchmarks, and real-world use cases, translating research outcomes into scalable solutions.

• Collaborate closely with international, cross-disciplinary teams of researchers, engineers, and product stakeholders to apply research innovations to business-relevant scenarios such as product engineering, diagnostics, maintenance, and repair.

• Contribute to technology transfer, supporting the transition from research prototypes to production-ready systems in collaboration with software and product teams.

• Publish research findings in top-tier conferences and journals, file patents, and contribute to Bosch's intellectual property portfolio.

• Stay up to date with the latest advancements in AI, machine learning, knowledge representation, and multimodal systems, ensuring Bosch remains at the forefront of innovation.

• Actively participate in internal and external research communities, workshops, and collaborations to foster knowledge exchange and thought leadership.

Qualifications

Educational qualification:

Ph.D. or M.S. from top Indian institutes (IITs, IIITs, IISc etc.) in Computer Science or a related field (e.g., NLP, linguistics, artificial intelligence, cognitive science)

Experience:

3-5 years

Mandatory/required Skills:

• Expertise in Neuro-Symbolic AI Architectures and Frameworks: Proven ability to design, implement, and integrate hybrid AI systems that combine machine learning with symbolic reasoning (e.g., knowledge graphs, rule engines, logic programming, ontologies) to address complex requirements.

• Advanced Data Engineering for Multi-Modal Integration: Demonstrated proficiency in building robust data pipelines capable of integrating, cleaning, and preprocessing heterogeneous data sources

• Hands-on Experience with Natural Language Processing for Knowledge Systems: Practical experience in applying state-of-the-art NLP techniques (e.g., Transformers, LLMs, information extraction) to understand user queries, extract insights from text, and contribute to the automatic construction and expansion of dynamic knowledge bases.

• Proven ability to work collaboratively in cross-functional and international teams.

Preferred Skills:

• Strong understanding of knowledge representation and reasoning techniques, including ontology design, schema alignment, rule authoring, explainable inference workflows, and hybrid symbolic-neural evaluation methodologies.

• Hands-on experience with enterprise knowledge graph and semantic web technologies, especially Stardog, including ontology modelling using OWL/RDFS, RDF data modelling, SPARQL query development, SHACL-based validation, reasoning/inference, and graph-based data integration.

• Working knowledge of symbolic query and reasoning approaches such as SPARQL, SHACL, description logics, logic programming, rule-based inference, or constraint-based reasoning.

• Ability to formulate research questions, design experiments, define evaluation criteria, run ablations, and interpret results with scientific rigor and strong reproducibility practices.

• Skills in graph embeddings, graph neural networks, Answer Set Programming/Prolog, SWRL or Drools, causal reasoning, agentic AI workflows, MLOps/cloud deployment, research publications, and patent drafting will be an added advantage.