Senior Software Engineer, Streaming
Crusoe CloudSan Francisco, CA - US$172k – $209kPosted 11 March 2026
Job Description
Senior Software Engineer, Streaming
Crusoe is on a mission to accelerate the abundance of energy and intelligence. As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack — from electrons to tokens — to power the world's most ambitious AI workloads. When you join Crusoe, you join a team that is building the future, faster.
We're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that — with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI.
We're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved — people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services.
If you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe.
ABOUT THIS ROLE
We’re looking for a Senior Streaming Software Engineer to join the Observability team within our Cloud Infrastructure organization. This team builds and operates the real-time data platforms that power metrics, logs, traces, and event streams used by engineers across the company to understand and operate Crusoe’s AI cloud reliably at scale.
In this role, you’ll design, build, and operate high-throughput streaming systems that process massive volumes of telemetry data generated across our GPU cloud and global data centers. Your work will help ensure engineers have real-time visibility into complex distributed systems and the infrastructure that powers them.
This is an opportunity to work on large-scale data pipelines and distributed systems that power observability across a rapidly scaling AI cloud environment.
WHAT YOU’LL BE WORKING ON
- Designing, building, and maintaining streaming services and pipelines that ingest and process observability data including logs, metrics, traces, and operational events
- Implementing real-time data processing systems using technologies such as Kafka, Kinesis, Pub/Sub, Flink, or similar streaming platforms
- Scaling streaming infrastructure to support high-throughput telemetry ingestion, high-cardinality workloads, and bursty infrastructure traffic patterns
- Ensuring streaming systems are reliable and observable, with strong instrumentation, dashboards, and alerting
- Collaborating with SREs and platform teams to integrate streaming data into internal observability tools and operational workflows
- Participating in on-call rotations, incident response, and post-incident reviews
- Improving system reliability and developer experience through automation, CI/CD, and infrastructure-as-code practices
- Contributing to technical design discussions and reviews for new streaming capabilities
WHAT YOU’LL BRING TO THE TEAM
- Strong experience building and operating distributed systems, especially streaming or real-time data platforms
- Hands-on experience with Kafka or similar distributed streaming technologies
- Proficiency in backend languages such as Java, Scala, Go, or Python
- Experience operating services in cloud or large-scale infrastructure environments
- Solid understanding of observability fundamentals including metrics, logging, tracing, and alerting
- Comfort debugging production issues across distributed systems
- Ability to own features end-to-end, from design through production operations
- Strong collaboration skills and a pragmatic engineering mindset
BONUS POINTS
- Experience building observability platforms at cloud or data center scale
- Familiarity with stream processing frameworks and delivery semantics
- Experience with Kubernetes and containeriz ... (truncated, view full listing at source)
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