Remote Distributed Systems Engineer

grabjobs • Full-time • Remote (Concord, New Hampshire, United States) • $0 / year • 13h ago

Description ThisWay Global is looking for a Distributed Systems Engineer in a remote role within the United States. ThisWay Global, Inc. is an AI-first technology company headquartered in Texas, operating at the intersection of artificial intelligence, data center infrastructure, and workforce solutions. The company operates across three primary business areas: ADCAP — AI & Data Center Acceleration Platform:

A data center development and operations platform supporting accelerated deployment timelines and NVIDIA NVL72/GB300 GPU clusters. Amalgamy.ai — AI Orchestration Software:

An enterprise AI orchestration platform focused on GPU and compute utilization across AI environments. Staffing & Workforce Solutions:

AI-powered talent matching solutions connecting employers with candidates at scale. This role focuses on building foundational distributed systems and operational infrastructure that support AI and exascale computing environments. The work emphasizes systems programming, distributed architecture, fault tolerance, and HPC-grade reliability. Location:

Remote – United States Department:

Engineering Employment Type:

Full-Time, Exempt

Responsibilities

Design and build distributed systems that tolerate latency, bandwidth constraints, and intermittent connectivity. Implement fault-tolerant communication strategies, retry logic, backpressure, caching, and eventual consistency patterns. Write maintainable, resilient, and tested code following development standards and methodologies. Debug and improve system behavior, including networking and distributed coordination issues. Contribute to systems written primarily in Rust. Work with system-level concerns including scheduling, memory management, I/O optimization, storage hierarchy management, and system reliability. Optimize performance and memory usage in resource-constrained environments. Debug concurrency issues and distributed coordination challenges. Design and maintain APIs and communication layers between distributed components. Identify and reduce tight coupling across services and systems. Diagnose and resolve cross-system failures in production environments. Design and implement secure, reliable solutions aligned with engineering standards. Collaborate with engineers and computer scientists on operating systems internals, compiler internals, fault tolerance, file system architecture, and trusted systems. Contribute to resolving architectural and systemic issues. Continue developing expertise in distributed systems, HPC infrastructure, and related tooling.

Requirements Experience building distributed systems in environments with low bandwidth, high latency, or unreliable communication links. Production experience developing systems in Rust or Go. Understanding of distributed systems failure modes and mitigation strategies. Knowledge of consistency models, coordination strategies, and state replication. Experience designing APIs and communication layers between distributed components. Experience working within established architectures and delivering production-quality components. Understanding of systems-level concepts including durability, reliability, and operational behavior. Ability to work independently while collaborating with technical leadership. Preferred Qualifications

Experience with HPC environments, exascale computing, or AI/ML infrastructure. Exposure to operating systems internals, compiler design, or language runtimes. Experience with edge computing or constrained network environments. Familiarity with message queues, event-driven systems, or streaming architectures. Exposure to consensus algorithms or distributed coordination primitives. Experience with concurrency, memory management, or performance optimization in production systems. Experience contributing to developer tooling, internal platforms, or infrastructure-layer components.

Benefits Remote work within the United States. Opportunity to work on distributed systems supporting AI and exascale workloads. Collaboration with engineers experienced in operating systems internals, compiler internals, fault tolerance, file system architecture, and trusted systems. Exposure to AI infrastructure, HPC, and large-scale distributed computing environments.

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