Recorded Future is seeking a Principal Software Engineer to join our Engineering team, responsible for building and scaling the pipelines that power the Security Intelligence Graph. As a Principal Software Engineer, you will work across application code and infrastructure to collect, transform, and deliver structured threat data: tracking malware infrastructure, phishing campaigns, spam networks, command-and-control systems, and more. You will play a key role in driving architectural improvements and ensuring the platform scales effectively as it continues to grow.

What You'll Do:

• Data Pipeline Ownership: Design, build, and maintain scalable data pipelines that collect, transform, and deliver structured threat intelligence data powering the Security Intelligence Graph.
• Architecture & Scalability: Drive meaningful architectural improvements across data and platform systems to ensure long-term scalability, reliability, and performance.
• Full-Stack Engineering: Work across both application code and infrastructure, contributing to backend services, data systems, and platform capabilities.
• Data Processing & Analysis: Apply statistical techniques to large-scale datasets to generate accurate and impactful insights.
• Reliability & Observability: Ensure production-grade pipeline reliability through strong observability, error handling, and failure recovery practices.
• Infrastructure & Deployment: Build and maintain CI/CD pipelines and contribute to infrastructure-as-code practices to support efficient and repeatable deployments.
• Cross-Team Collaboration: Communicate technical concepts clearly and collaborate effectively across teams, geographies, and varying levels of technical expertise.
• Technical Leadership: Engage with technical leaders across teams and contribute to raising the engineering bar through knowledge sharing and best practices.

What You'll Bring:

• At least 5 years of experience in data engineering, backend engineering, or platform engineering.
• Strong proficiency in Python, with experience in Go or Rust considered a plus.
• Experience working with cloud infrastructure, including compute, storage, networking, and IAM fundamentals.
• Hands-on experience with message queues and pub/sub systems such as Kafka or RabbitMQ.
• Experience with SQL databases such as Postgres or AWS RDS, and NoSQL databases such as MongoDB or Elasticsearch.
• Familiarity with data lake storage frameworks such as Delta Lake and graph databases such as AWS Neptune or Neo4j.
• Experience developing REST APIs using frameworks such as Flask, Django, or FastAPI.
• Proven experience architecting and delivering production-grade applications and ETL/ELT pipelines.
• Strong understanding of building reliable data pipelines, including observability, error handling, and failure recovery.
• Experience building and maintaining CI/CD pipelines; GitHub Actions experience is a plus.
• Familiarity with infrastructure-as-code tools; Terraform experience is a plus.
• Comfort working with high-volume, complex, and unstructured data.
• Strong problem-solving skills, including the ability to debug both software and data issues.
• Excellent attention to detail and ability to work independently while delivering high-quality results.
• Interest in or familiarity with security data domains such as threat infrastructure, malware, or network telemetry.
• Bachelor’s and/or Master’s degree in Computer Science, Mathematics, Statistics, Engineering, or equivalent experience.

Preferred Qualifications:

• Experience or interest in cybersecurity data domains such as malware analysis, phishing detection, or network telemetry.
• Experience working with graph-based data systems or security intelligence platforms.

The base salary range for this full-time position is $152,000-$228,500. Our salary ranges are determined by role, level, and location. The salary displayed reflects the range for new hire salaries for the position across all US locations. Within the range, individual pay is determined by state, work location and additional factors, including job-related skills, experience, and relevant education or training. This position may be eligible for incentive compensation, equity, and medical, dental, vision, life insurance and 401K. Your recruiter can share more about the specific details of the compensation and benefit package during the hiring process.

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The Opportunity:

ZoomInfo built the industry's most sophisticated GTM data acquisition infrastructure. Now we're applying that same rigor to context engineering—the emerging discipline that determines whether AI systems deliver transformative value or incremental improvement.

This role architects the context layer powering our AI intelligence across Copilot, GTM Studio, and MarketingOS. You'll transform how ZoomInfo's agentic workflows access, compress, and deliver precisely the right information at exactly the right moment. The impact is organization-wide: every AI interaction, every intelligent recommendation, every autonomous agent action depends on the context infrastructure you’ll build.

We've transitioned to AI-first product thinking company-wide. The context pipelines exist but remain nascent—creating a rare opportunity to define architectural patterns and platform standards that compound value across multiple product teams in the years to come.

What You'll Do:

Architect Context Acquisition Pipelines Design and optimize how ZoomInfo retrieves, transforms, and delivers context from our semantic data layer, memory systems, and data producers. You'll balance retrieval quality against latency and cost constraints, implementing hybrid search strategies, intelligent caching, and context compression techniques that maintain information density while respecting token budgets.

Own the Context Layer Platform Build infrastructure serving multiple product teams—Copilot, GTM Studio, MarketingOS—as internal customers. Establish API contracts, developer experience standards, and integration patterns that accelerate feature velocity. Maintain the delicate balance between providing flexible building blocks and opinionated solutions that encode best practices.

Drive Quality Through Measurement Implement evaluation frameworks using RAGAS metrics and custom benchmarks. Monitor retrieval precision, context relevance, hallucination rates, and system performance in production. Translate quality signals into architectural improvements, working closely with ML engineers to iterate on embedding models, reranking strategies, and retrieval algorithms.

Navigate Emerging Research Context engineering evolves weekly. You'll continuously evaluate innovations—GraphRAG for multi-hop reasoning, test-time compute scaling, multimodal retrieval, compression techniques—determining which advances warrant production investment versus which remain academic curiosities. Bring external best practices to ZoomInfo while contributing learnings back to the broader community.

Orchestrate Cross-Functional Execution Translate between three distinct worlds: ML engineers optimizing retrieval algorithms, platform engineers building scalable infrastructure, and product teams shipping customer features. Establish communication cadences, prioritization frameworks, and decision-making processes that balance urgent requests against strategic platform development.

What You’ll Bring:

• 6-8 years of product management experience with 2+ years in ML/AI infrastructure
• Direct experience with production RAG systems, vector databases, or semantic search, context management
• Experience with graph databases (e.g. Neo4j) 
• Track record building platform products serving multiple internal or external customers
• Familiarity with context compression, embedding models, and retrieval evaluation frameworks
• History of defining product vision in nascent technical domains where best practices are still emerging

Who You Are:

Technical Foundation Expert-level understanding of RAG system architecture—you can discuss embedding dimensionality trade-offs, vector database indexing strategies, and reranking approaches with depth. You've built or significantly contributed to production retrieval systems, not just managed them at arm's length. Python and SQL proficiency enables you to review code, analyze retrieval issues, and prototype solutions for concept validation.

Platform Product Mindset Experience building infrastructure products where internal engineering teams are your customers. You measure success through downstream product velocity improvements and developer satisfaction scores, not just uptime metrics. You understand platform economics—how each additional team using your infrastructure increases its value through shared learnings and amortized costs.

Intellectual Velocity You read recent research papers from arXiv, ACL, NeurIPS. You prototype emerging techniques to understand their practical constraints. You maintain strong opinions weakly held, updating your architectural assumptions as evidence accumulates. The discipline moves too fast for static expertise—continuous learning is non-negotiable.

Strategic Communication You translate between technical depth and business impact fluently. You can explain to executives why implementing GraphRAG takes 6 months but unlocks $10M in product capabilities. You can communicate to engineers why business constraints require shipping "good enough" in 3 weeks rather than "optimal" in 3 months. You influence without formal authority through data, clear reasoning, and earned credibility.

The Environment:

Reporting & Collaboration Report to the Senior Product Director for Context Engineering, Semantic Data Layer, and Agentic Memory within ZoomInfo's Intelligence team. Work alongside PMs responsible for signals and ML scoring/recommendation models. Together, you ensure our agentic workflows fill context windows with high-quality, information-dense content exactly when needed.

Pace & Problems Fast-moving engineering team that understands the space. Company-wide AI adoption push creates both urgency and opportunity. Expect interesting problems: How do we maintain sub-200ms retrieval latency at scale? When does GraphRAG justify its indexing cost? How do we balance context freshness with cache efficiency? You'll shape answers that become architectural patterns across the organization.

Impact Define a nascent discipline at a company that's already AI-first in product thinking and organizational structure. Your architectural decisions compound—every improvement to context quality multiplies across Copilot, GTM Studio, MarketingOS, and future products we haven't imagined yet. This is infrastructure work with direct line-of-sight to customer value.

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Role Summary

Babel Street is building an AI-native, agent-ready, API-first intelligence platform where computer vision, NLP, and document enrichment continuously produce signals about people, places, organizations, and events. The knowledge graph transforms those signals into a coherent, queryable representation of the world.

We are hiring a Knowledge Graph Engineer to help design, build, and operate the entity resolution and graph layer that connects our multimodal pipelines. You will work closely with the Director of Computer Vision and collaborate with ML, platform, and data engineering teams to connect mentions in text, objects in images, locations in geotagged media, and entities in structured sources into a unified graph.

This is a hands-on engineering role where you will contribute to production systems, grow your expertise in graph modeling and entity resolution, and help shape a platform that is still evolving. This will be a hybrid role based out of either our Reston, VA or Somerville, MA office.

Key Responsibilities

• Implement and evolve graph schemas and entity models for people, locations, organizations, images, and events
• Build and maintain entity resolution pipelines that reconcile entities across NLP outputs (GLiNER, spaCy), computer vision outputs (object detection, OCR, face recognition, geolocation), and structured data sources
• Develop ingestion pipelines that write resolved entities into graph systems such as ArangoDB and Spanner Graph or BigQuery Graph
• Contribute to cross-modal linkage between text, images, and geospatial data, including confidence scoring and provenance tracking
• Collaborate with computer vision engineers to integrate visual entity outputs into the graph layer
• Help build graph-backed APIs and services used by downstream products and workflows
• Write tests, contribute to design discussions, and support improvements to data quality and pipeline reliability

Why This Role Matters

The knowledge graph is where Babel Street’s signals become usable intelligence. A detected landmark, a name extracted from text, an organization mentioned online, and a face identified in an image all become more valuable when they resolve to a shared representation of an entity.

In this role, you will help:

• Connect multimodal signals into a unified graph
• Improve the accuracy and consistency of entity resolution
• Enable downstream search, analytics, and agent workflows

Required Qualifications

• 2–5 years of professional software engineering experience (or equivalent experience through internships and projects)
• Proficiency in Python and at least one typed language (Java, Go, C#, or TypeScript)
• Familiarity with at least one graph database or graph processing framework (ArangoDB, Neo4j, TigerGraph, JanusGraph, NetworkX, or similar)
• Solid understanding of SQL and core data modeling concepts (keys, normalization, joins, indexing)
• Ability to work across teams, incorporate feedback, and make steady progress in ambiguous problem spaces

Preferred Qualifications

• Exposure to entity resolution, record linkage, or deduplication
• Familiarity with semantic modeling or schema design (RDF, OWL, property graphs, JSON-LD)
• Experience with cloud platforms such as GCP (Spanner, BigQuery, Cloud Run)
• Interest in computer vision, NLP, geospatial data, or multimodal systems
• Experience testing data pipelines, implementing data quality checks, or working with observability tools
• Exposure to search, ranking, or retrieval-based systems

What Success Looks Like (First Six Months)

• Month 1–2: Ramp up on the graph stack, contribute to existing pipelines, and ship initial changes
• Month 3–4: Take ownership of a component or pipeline with guidance, including testing and documentation
• Month 5–6: Deliver improvements to entity resolution quality, graph ingestion, or cross-modal linkage, and contribute to design discussions

Benefits at Babel Street (just to name a few...)

• Health Benefits: Babel Street covers 85-100% monthly premium costs for Medical, Dental, Vision, Life & Disability insurances – for you and your family!
• Retirement Plans: Babel Street offers both a Traditional and Roth 401(K) with a very competitive match.
• Unlimited Flexible Leave: We trust our employees to manage their own time and balance their personal and work lives.
• Holidays: Babel Street provides employees with 12 paid Federal Holidays
• Tuition Reimbursement: We are committed to investing in our employees. One way we do that is with our Tuition Reimbursement Program for continuing education.                 

Babel Street is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. Further, Babel Street will not discriminate against applicants for inquiring about, discussing or disclosing their pay or, in certain circumstances, the pay of their co‐worker, Pay Transparency Nondiscrimination. In addition, Babel Street's policy is to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations and ordinances where a particular employee works. Upon request, we will provide you with more information about such accommodations.