INNOVATION IS IMAGINING WHAT NO ONE ELSE CAN.

At the BMW Group, everything starts with passion. It turns a profession into a vocation. It drives us to keep reinventing mobility and bring innovative ideas onto the roads. Enthusiasm for joint projects turns a team into a strong unit where every opinion is valued. It is only when expertise, highly professional processes and enjoyment of work unite that we can shape the future together.

Our platform teams build AI-based toolchains that simulate, reprocess and train models on petabyte-scale automotive data

• and we increasingly rely on agentic AI to operate, maintain and improve these toolchains. The glue that makes agents effective is context: the right knowledge, delivered at the right time, in the right shape. We are looking for an engineer who can design and evolve the knowledge-management and context-engineering backbone that powers both our product (an inter-domain multi-agent vehicle system) and our internal development toolchain.

Validation Engineer

• Context, Semantics & Memory (f/m/x)

Start date: from now on

Type of employment: unlimited Working hours: Full-time

If you apply, the next stages of the recruiting process include an online test followed by technical interviews with the hiring manager (either virtual or in person). You design and operate knowledge graphs and graph databases that capture domain knowledge, toolchain topology, operational incidents, root-cause analyses and lessons learned

• enabling graph-based retrieval across the organization.

Further, you build context-engineering pipelines that dynamically assemble, rank and budget the information supplied to LLM-based agents. Also, you implement feedback loops: coding agents and skills that interact with our toolchain log outcomes, diagnose failures and write findings back into the knowledge graph, making the system progressively more capable. It is your responsibility to develop and maintain ontologies and semantic models for the automotive AI domain

• sensor configurations, simulation scenarios, data-pipeline topology, model lineage - ensuring consistent vocabulary across teams and tools.

You architect agent memory systems (working, episodic, semantic) with appropriate consolidation and retention mechanisms so that long-running agent sessions retain useful experience without context-window bloat. In addition, you evaluate and optimise retrieval quality using relevance, coverage, freshness and redundancy metrics, and run experiments comparing retrieval strategies (dense, sparse, graph, hybrid). You contribute to cost-efficient model selection and context-compression strategies (prompt compression, semantic caching, model routing) to keep latency low and budgets sustainable at scale. University degree in Computer Science, Information Science, AI/ML, or a related field. 1-3 years of professional experience in knowledge management, knowledge-graph engineering or context engineering for AI/LLM systems. Expert-level programming skills in at least one high-level language (C++, Java, Kotlin or Python). Proficiency in AI-assisted development using coding agents such as Claude Code, GitHub Copilot or OpenCode. Hands-on experience building and querying knowledge graphs (RDF/SPARQL or property graphs) and integrating them with LLM-based retrieval (e.g. GraphRAG, hybrid search). Experience with systems design, particularly for knowledge-intensive or retrieval-heavy architectures. Familiarity with agent memory architectures, ontology design, semantic modelling, graph-based analytics, entity resolution or knowledge-graph embeddings. Challenging projects with which we are shaping the mobility of tomorrow together. Wide range of personal and professional development opportunities. Attractive, fair and performance-related remuneration. High level of job security. Annual special payments such as vacation pay, Christmas bonus, and profit sharing. Flexible working hours including 6...