Distributed Systems Engineer / Architect
Location: Remote
Department: Engineering
Senior Distributed Systems Engineer / Architect
Series A Cybersecurity Company — RapidFort
Location: Remote / Hybrid
Type: Full-time
About RapidFort
RapidFort is a Series A cybersecurity company backed by $42M from leading investors, building the next generation of container and software supply-chain security.
Our platform helps enterprises and U.S. government agencies eliminate vulnerabilities in container images, secure Kubernetes environments, and protect cloud-native infrastructure at runtime.
Due to our work with DoD and U.S. federal customers, U.S. citizenship is required for this role.
Overview
We are looking for a Senior Systems Engineer / Architect (Distributed Systems & Performance) to design and build highly scalable custom systems that process large volumes of data across CPU, disk, and network intensive workloads. This role is deeply hands-on and requires strong systems thinking, algorithm design, and performance optimization skills.
You will work on core infrastructure and algorithms, building systems that maximize resource utilization across distributed environments. The ideal candidate enjoys working close to the metal, writing efficient code and tooling (primarily in Python and Bash) while building the instrumentation needed to continuously measure, analyze, and improve system performance.
This role requires a data-driven mindset and a passion for building reliable, scalable systems from first principles.
Responsibilities
System Architecture
Design and implement scalable distributed systems that handle heavy CPU, disk, and network workloads.
Architect systems for high throughput, reliability, and efficient resource utilization.
Develop distributed algorithms and data processing pipelines.
Performance & Optimization
Analyze system behavior to identify bottlenecks across compute, storage, and network layers.
Optimize workloads for maximum efficiency and minimal resource waste.
Develop strategies for parallelization, batching, and workload scheduling.
Engineering & Implementation
Implement system components and tooling primarily in Python and Bash.
Build custom orchestration, automation, and distributed job execution mechanisms.
Write efficient algorithms and low-level logic to manage large-scale workloads.
Observability & Data-Driven Engineering
Build instrumentation, metrics, and telemetry to measure system performance.
Develop dashboards and analysis workflows to guide optimization decisions.
Use empirical data and experimentation to improve system behavior.
Infrastructure & Reliability
Design systems that operate reliably across distributed environments.
Implement monitoring, debugging, and recovery mechanisms for large-scale systems.
Collaborate with infrastructure and platform teams to ensure smooth deployment and operation.
Requirements
Core Experience
Strong experience building distributed systems or large-scale backend infrastructure
Deep understanding of systems performance (CPU, memory, disk I/O, networking)
Experience optimizing workloads for throughput and efficiency
Programming
Strong Python development skills
Strong Bash / shell scripting
Ability to implement and reason about algorithms and system-level logic
Systems Knowledge
Experience with parallel processing, distributed job execution, or large data pipelines
Familiarity with Linux systems, resource scheduling, and performance tuning
Understanding of networked systems and distributed coordination
Engineering Approach
Strong data-driven mindset with focus on measurement and experimentation
Experience building observability, metrics, and instrumentation
Ability to debug complex systems in production environments
Nice to Have
Experience with high-performance computing (HPC) workloads
Experience with containerized environments (Docker/Kubernetes)
Background in large-scale data processing or distributed compute frameworks
Familiarity with performance profiling tools and system tracing
What You’ll Work On
Designing custom distributed compute frameworks
Building efficient algorithms to process large-scale data workloads
Optimizing compute pipelines across CPU, disk, and network resources
Developing instrumentation and performance analytics
Improving system efficiency through continuous measurement and experimentation
Base Salary: $170,000 to $200,000
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