About Turing
Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises looking to deploy advanced AI systems. Turing accelerates frontier research with high-quality data, specialized talent, and training pipelines that advance thinking, reasoning, coding, multimodality, and STEM. For enterprises, Turing builds proprietary intelligence systems that integrate AI into mission-critical workflows, unlock transformative outcomes, and drive lasting competitive advantage.
Recognized by Forbes, The Information, and Fast Company among the world’s top innovators, Turing’s leadership team includes AI technologists from Meta, Google, Microsoft, Apple, Amazon, McKinsey, Bain, Stanford, Caltech, and MIT. Learn more at www.turing.com
Location
Remote / Hybrid (HQ visits as needed)
Experience
5 + years in software engineering; 2 + years in GenAI
Engagement
Full-Time, Permanent
ABOUT THE ROLE
We are looking for a talented Sr. GenAI Engineer who sits at the intersection of knowledge engineering, agentic AI, and data intelligence. In this role you will design and operate AI agents that traverse, reason over, and enrich large-scale knowledge graphs — then extend that context dynamically using live data sources such as the web, enterprise APIs, and structured databases.
The ideal candidate is deeply comfortable with graph data models, LLM orchestration frameworks, and retrieval-augmented pipelines. Bonus points if you have experience working in trade-craft or intelligence-adjacent environments where provenance, precision, and adversarial robustness are non-negotiable.
KEY RESPONSIBILITIES
Knowledge Graph Engineering
- Design, build and maintain large-scale property graphs and RDF triplestores (Neo4j, Amazon Neptune, Stardog, or equivalent).
- Develop and govern ontologies, taxonomies, and entity-relationship schemas that reflect real-world domain semantics.
- Implement graph ingestion pipelines that extract, transform, and link entities from structured, semi-structured, and unstructured data.
- Optimise graph traversal queries (Cypher, SPARQL, Gremlin) for sub-second response at production scale.
- Train and deploy graph neural networks (GNNs) for node classification, link prediction, and subgraph retrieval - Maintain model retraining workflows triggered by graph drift or coverage degradation.
Agentic AI Systems
- Architect and implement autonomous agents that plan multi-step reasoning chains over knowledge graph data using LLMs (GPT-4o, Claude, Gemini, or open-source equivalents).
- Build graph-aware Retrieval-Augmented Generation (RAG) pipelines that blend structured graph context with unstructured document retrieval.
- Design tool-use and function-calling layers so agents can query live data sources — web search, REST/GraphQL APIs, relational databases — to extend or verify graph knowledge.
- Implement agent memory, reflection, and self-correction loops to improve reliability over multi-hop tasks.
Context Enrichment & Data Fusion
- Integrate web scraping, news feeds, and open-source intelligence (OSINT) sources to keep the knowledge graph current.
- Build entity resolution and deduplication components that merge data from heterogeneous sources into a consistent graph.
- Develop confidence-scoring and provenance-tracking mechanisms so downstream consumers understand the reliability of any piece of context.
MLOps & Production Readiness
- Package agents as scalable microservices; instruments with observability tooling (tracing, latency, token cost).
- Collaborate with platform engineers to deploy workloads on cloud-native infrastructure (AWS / GCP / Azure).
- Maintain evaluation harnesses that measure agent accuracy, hallucination rate, and graph coverage over time.
REQUIRED SKILLS & EXPERIENCE
- 5 + years of professional software engineering with strong Python (or Java / Kotlin) proficiency.
- Hands-on production experience with at least one major graph database — Neo4j, Amazon Neptune, TigerGraph, or comparable.
- Demonstrated knowledge of graph query languages like Cypher, SPARQL, or Gremlin — at production query complexity.
- Direct experience building LLM-powered agents or pipelines using frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or Semantic Kernel.
- Solid understanding of RAG architectures: chunking strategies, vector stores (Pinecone, Weaviate, pgvector), hybrid retrieval, and re-ranking.
- Familiarity with prompt engineering, few-shot learning, and LLM evaluation techniques.
- Experience integrating external data sources via APIs, web scraping (Playwright / Scrapy), or streaming pipelines (Kafka / Kinesis).
- Working knowledge of containerisation (Docker, Kubernetes) and CI/CD pipelines.
- Familiarity with graph export formats - at least one GraphML, RDF/OWL, or JSON-LDExperience integrating GNN-derived features into vector stores or RAG pipelines
PREFERRED QUALIFICATIONS
- Advanced degree (MS / PhD) in Computer Science, Information Science, Computational Linguistics, or a related field.
- Experience in intelligence, defence, or trade-craft environments — working with OSINT, link analysis, entity disambiguation, or signals intelligence data.
- Understanding of access-control models for sensitive graph data (need-to-know, compartmentalisation, provenance labelling).
- Familiarity with knowledge representation standards like OWL, SHACL, RDF-star, JSON-LD, W3C PROV.
- Experience with fine-tuning or instruction-tuning open-source LLMs (Llama, Mistral, Falcon) for domain-specific tasks.
- Background in network-analysis algorithms: centrality, community detection, path-finding, anomaly detection on graphs.
- Contributions to open-source graph or GenAI projects; published research or technical blog presence.
- Active or adjudicatable security clearance (Secret or above) — strongly preferred for trade-craft assignments.
★ Trade-Craft Experience — A Significant Plus
Candidates with backgrounds in intelligence analysis, signals intelligence, law enforcement data fusion, or related trade-craft disciplines are strongly encouraged to apply. Understanding of link analysis, entity disambiguation under adversarial conditions, handling classified or compartmentalized data, and mission-driven product constraints will set you apart.
Values
- We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value.
- We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection
- We are Al forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity.
Advantages of joining Turing
- Amazing work culture (Super collaborative & supportive work environment; 5 days a week)
- Awesome colleagues (Surround yourself with top talent from Meta, Google, LinkedIn etc. as well as people with deep startup experience)
- Competitive compensation
- Flexible working hours
Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
For applicants from the European Union, please review Turing's GDPR notice here.
