AI Engineer, Ontologies & Knowledge Graphs
Cadence Open · verified Oct 22, 2026 At a glance
Mid-level AI Engineering role at Cadence. LIVONIA 01 · full-time · no salary stated · we estimate $132,450–$205,151 across the United States.
Cadence has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Turn source code, APIs, file formats and documentation into structured data pipelines and a queryable knowledge store
- Model product entities, relationships and capabilities, then build and maintain knowledge graphs across varied data
- Parse source code and metadata automatically, including extracting structure from abstract syntax trees
- Create typed interfaces and retrieval layers, including embeddings or RAG, so downstream systems can use product knowledge
- Work with domain engineers to break product workflows into callable steps and check data coverage and quality
What you bring
- A bachelor’s or master’s degree in computer science, mechanical engineering or a comparable field
- Strong Python skills and experience building and using REST APIs
- Hands-on work building ETL or ELT pipelines for structured and unstructured data
- Familiarity with graph databases or semantic modeling, plus experience with at least one agent framework
- Understanding of LLM context, retrieval and tool use, with exposure to CAE, FEA, CFD or a related engineering field
Who this fits
This role suits an engineer who can make sense of unfamiliar codebases and turn complex workflows into discrete, callable operations. You’ll work alongside domain engineers who supply subject-matter expertise, so specialist knowledge of the engineering domain is not required. Travel is infrequent and may happen for team alignment workshops.
From the employer
At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.
We are looking for an engineer who builds the data and interface layer that makes complex software products programmatically understandable. Many powerful products expose rich but heterogeneous surfaces — source code, scripting APIs, file formats, data structures, and workflow logic. This role builds pipelines that extract and transform those surfaces into structured, queryable knowledge — designing the schemas, constructing the knowledge graphs, and exposing them through clean, typed programmatic interfaces for downstream consumption.
Job Responsibilities
- Build ETL/ELT pipelines that extract data from source code, APIs, file formats, and documentation and load it into a structured knowledge store.
- Design and maintain schemas and semantic data models capturing entities, relationships, and capabilities.
- Construct and maintain knowledge graphs over heterogeneous product data.
- Develop source and metadata parsers (including source-code/AST parsing) to extract structure automatically.
- Build typed programmatic interfaces and data-access layers over the knowledge layer.
- Implement retrieval and indexing layers (e.g., embeddings, RAG) over product knowledge.
- Work with domain engineers to decompose complex product workflows into discrete, callable operations.
- Assess data sources for coverage, quality, and schema completeness across multiple products.
Job Qualifications
- BS/MS in Computer Science, Mechanical Engineering, or similar.
- Strong Python; experience building and consuming REST APIs.
- Experience building data pipelines (ETL/ELT) over structured and unstructured data.
- Familiarity with graph databases and/or semantic/ontology modeling (RDF, OWL, property graphs, or equivalent).
- Experience with at least one agent framework (LangChain, LangGraph, AutoGen, CrewAI, or similar).
- Understanding of how LLMs consume context and call tools (retrieval, RAG, embeddings).
- Exposure to CAE/FEA/CFD or a related physical-simulation or engineering domain.
- Comfortable working within unfamiliar or undocumented codebases.
- Systems thinker — able to decompose a complex legacy workflow into discrete, callable steps.
Additional Skills/Preferences
Nice to have:
- Vector databases.
- Data-access and API interface development.
- Parsing structured file formats.
- Surrogate modeling or related numerical methods.
Deliberately not required:
- Deep or specialist domain expertise beyond working familiarity — domain engineers provide that.
- No PhD or ML research background required.
Additional Information
- Works across multiple products, building structured knowledge and interfaces over their capabilities.
- Collaborates closely with domain engineers who provide subject-matter expertise.
- Works with data pipelines, graph databases, and product API surfaces.
- Travel is not an expectation for this role. Occasional travel may occur for broad team alignment workshops, but these are infrequent.