Distributed Systems Software Engineer

Distributed Systems Software Engineer

Location: Brooklyn, NY (Onsite Only)

Role Type: Full‑Time

Start Date: ASAP

Company Overview: 

Stealth AI Cybersecurity Startup is building the next generation of AI‑driven defense, helping organizations detect and defend in an era where AI fights AI on the cyber battlefield. Backed by First Round Capital and Brightmind, the company is founded and staffed by top operators and engineers from Palo Alto Networks, AWS, Demisto, Abnormal Security, Twitter, Google, Wiz, Meta, SentinelOne, and other leading security and AI teams.

We move fast, operate at high technical rigor, and are looking for passionate builders who thrive in high‑velocity environments as we expand our customer base and push the boundaries of AI‑powered cybersecurity.

Job Overview

Our product ingests and analyzes exabytes of streaming data from cloud platforms, identity providers, network logs, and more. We’re building a new, large‑scale data platform from the ground up and seeking an exceptional Distributed Systems Software Engineer to help shape and build it.

You’ll work alongside engineers who architected some of the world’s largest distributed systems. You will design, build, and scale the core systems powering our products AI analytics engine—including data ingestion, normalization, enrichment, detection, and federated analytics. You’ll make key architectural decisions, drive performance and reliability at scale, and ensure our systems remain secure and cost‑efficient as we 100× our customer base. This is a rare opportunity to join at the foundation of a greenfield project and define how a world‑class distributed data platform is built.

Responsibilities

• Design & implement large‑scale distributed systems for log ingestion, normalization, and enrichment—transforming massive volumes of raw security telemetry into structured, enriched data ready for analytics, using modern data lake and streaming technologies to support near‑real‑time ingestion at scale.
• Architect & optimize data storage and retrieval for exabyte‑scale workloads—making key architectural decisions that enable efficient queries across distributed and federated data sources.
• Build federated analytics & streaming infrastructure that retrieves logs dynamically—design real‑time pipelines, event‑driven services, and scalable processing layers to support multi‑stage detection and correlation scenarios.
• Develop and maintain a reliable detection engine with the detection engineering team—implement and optimize query‑driven detectors across massive datasets, ensuring consistent performance, high availability, and accuracy.
• Implement intelligent, agentic workflows—collaborate with AI/ML to integrate large‑language models into the data and detection pipeline; design systems that support iterative experimentation, safe deployment, and continuous improvement of AI‑driven detection.

Basic Qualifications

• Proven experience designing, building, and operating large‑scale distributed systems and data infrastructure. You’ve built and debugged complex, high‑throughput data pipelines (not just consumed them) and are comfortable with modern data lake and streaming technologies handling massive security/telemetry data.
• Proficiency with cloud platforms (AWS, GCP, or Azure) and container orchestration (Kubernetes, ECS) for analytics systems.
• Experience with distributed databases and data‑warehouse technologies (e.g., ClickHouse, Snowflake, BigQuery, Apache Iceberg), caching (Redis/Memcached), and message queues (Kafka, Pulsar).
• Strong software engineering fundamentals and systems thinking; focus on performance, reliability, cost efficiency, and security at scale.
• Clear communication and collaboration skills; ability to work across disciplines and explain complex technical concepts to non‑engineers.

Preferred Qualifications

• Background in cybersecurity, SIEM, or endpoint detection; familiarity with security data formats like OCSF and entity representations.
• Hands‑on experience with federated analytics, query planners, and optimizing large, heterogeneous data estates.
• Track record integrating LLMs into production data systems; designing safe, iterative agentic workflows.

Compensation

• Base Salary: $180,000 – $250,000 (commensurate with level)
• Competitive equity package
• Full‑time onsite in Brooklyn

Additional Job Application Terms

This job is part of LinkedIn’s Full-Service Hiring beta program. Eligibility is limited to candidates located in and performing services in the United States, excluding those based in Alaska, Hawaii, Nevada, South Carolina, or West Virginia.

We’re committed to making our hiring process as smooth and timely as possible, and we understand that waiting to hear back can add to the anticipation. If you’re a potential fit, our team will reach out within two weeks to progress you to the next stage. If you don’t hear from us in that time, we encourage you to explore other opportunities with our team in the future, and we wish you the very best in your job search.