Black Lotus Labs Threat Researcher -APT Research

The Role

Black Lotus Labs is seeking a remote Threat Intelligence Researcher on the Research & Analysis team focused on tracking advanced threats. This team leverages Lumen’s global visibility across one of the world’s largest and most interconnected IP backbones, a petabyte-scale compute, AI-assisted workflows, proprietary telemetry, and third-party data sources to conduct cutting-edge threat research, hunt and track advanced persistent threat actors (APTs), identify emerging criminal infrastructure, and convert large-scale observations into actionable intelligence for customers. 

This role sits at the intersection of threat intelligence, data science, AI engineering, and security automation. The successful candidate will help shape the next generation of intelligence operations by building AI-assisted workflows that augment analyst tradecraft, accelerate threat discovery and detection development, automate enrichment and intelligence production, and transform large-scale telemetry into actionable security insights while maintaining analytical rigor, explainability, and operational guardrails.

Location

This is a remote position with occasional travel required. Candidates must reside in Maryland, Virginia, or the Washington, DC metropolitan area and be able to travel to customer sites approximately 3-4 days per month.

The Main Responsibilities

•  Leverage global datasets (e.g., netflow, malware and passive DNS,) to track malicious cyber actors, their infrastructure, and campaigns. Build repeatable AI-assisted and automation-driven methods to accelerate detection, prioritization, and intelligence production. 
• Use technical knowledge of adversary capabilities, infrastructure, malware, and network behavior to define, develop, and implement techniques for tracking sophisticated adversaries and delivering actionable threat intelligence to Lumen customers. 
• Lead and enhance threat hunting operations by engaging with research, engineering, data science, and customer-facing teams; building strong partnerships; exploring new data sources; and collaborate with team members in AI-assisted workflow generation, automation design, and complex analytic problem solving. 
• Identify malicious activity in large-scale network, endpoint, DNS, routing, and enrichment data, and scale detection through Python-based automation, data pipelines, machine learning techniques, and AI-assisted analytic development. 
• Establish AI powered analytical workflows to automate and scale detections of adversary activity and tradecraft. 
• Provide actionable analysis and strategic insights into emerging threats, vulnerabilities, adversary infrastructure, and automation-derived findings, translating complex technical information into clear intelligence for executive leadership, customers, and external stakeholders. 

What We Look For in a Candidate

• Proven experience in threat analysis and in-depth technical security research, with a track record of identifying and tracking nation-state malicious infrastructure activity with an emphasis on network traffic analysis.  
• Fluency in advanced threat hunting methodologies, attacker tactics, techniques, and procedures (TTPs), and the ability to derive actionable threat hunts, detections, and intelligence requirements from complex data sets.
• Experience with large-scale data analysis platforms with tools such as PySpark.
• Experience with telemetry collection and analysis, including OSINT, proprietary endpoint and network data, DNS and routing data, TTP-based threat hunting, and/or threat hunt tooling that improves cross-organization visibility and supports public-private or multi-company collaboration.
• Proven communication and presentation skills, including the ability to clearly and concisely convey complex technical findings, automation-derived insights, and intelligence judgments to technical, executive, customer, and partner audiences. 

Well-experienced candidates may also have the following skills:

•  Experience designing or implementing AI-assisted workflows, LLM-enabled research tools, retrieval-augmented generation, analyst copilots, or human-in-the-loop automation for security or intelligence use cases.
• Proficiency in malware reverse engineering and incident response. 
• Software development experience with Docker, distributed data technologies such as Hadoop or Spark, cloud AI services, vector databases, workflow orchestration, and machine learning frameworks such as TensorFlow or PyTorch.
• Experience with agentic AI or workflow frameworks such as LangGraph, Semantic Kernel, AutoGen, CrewAI, MCP-based architectures, or comparable orchestration approaches.
• Active TS/SCI  

Compensation

What to Expect Next