# Senior Software Engineer — File System

**Company:** [Nasuni](https://hotfix.jobs/companies/nasuni)
**Location:** Boston, MA, Marlborough, MA
**Role:** Backend Engineering
**Experience:** 7+ years
**Skills:** C, C++, Distributed Systems, File Systems, Nfs, Smb, S3, Kubernetes, Linux Kernel, Posix
**Posted:** 2026-06-10

> Senior engineer building and operating Nasuni’s distributed file system (Strider/CUFS) in high-performance C/C++. Owns storage subsystems, protocols, snapshots, caching, recovery, and Kubernetes-based HA services.

## Job Description

## Responsibilities
- Design, implement, test, and operate major components of Nasuni’s distributed file system and data path infrastructure.
- Write high-performance C/C++ for kernel-adjacent and user-space storage systems.
- Improve file system behavior across snapshots, caching, faulting, eviction, metadata handling, and recovery paths.
- Build and harden NFS, SMB, and S3 access layers with attention to protocol correctness, performance, and operational edge cases.
- Develop highly available storage services using Kubernetes-based patterns for failover, replication, scheduling, stateful workloads, and recovery.
- Contribute to distributed system design involving consistency models, metadata coordination, failure handling, and multi-volume behavior.
- Partner with adjacent engineering teams to deliver software that is observable, upgradeable, testable, and production-ready.
- Lead code reviews, design reviews, incident follow-up, and technical alignment within your areas of ownership.
- Use AI tools for code assistance, test generation, log analysis, debugging, documentation, or workflow automation while validating correctness, security, and performance before adoption.
- Mentor engineers and raise the quality bar for systems design, code quality, testing, and operational readiness.

## Requirements
- 7+ years of professional software engineering experience, including significant systems programming experience.
- Strong C or C++ expertise, including memory management, concurrency, debugging, profiling, and performance-sensitive code.
- Hands-on experience designing, building, or operating distributed systems in production.
- Practical understanding of consistency, availability, failure modes, replication, recovery, or distributed metadata.
- Experience with file systems, storage engines, databases, operating systems, kernel-adjacent software, or similar infrastructure.
- Familiarity with at least one protocol or storage interface such as NFS, SMB, S3, POSIX, FUSE, or object storage APIs.
- Ability to own complex technical work from design through production delivery.
- Strong written communication skills for design docs, reviews, remote collaboration, and operational handoffs.

## Nice-to-Haves
- Experience with cloud-native storage, object storage backends, or hybrid cloud infrastructure.
- Experience operating stateful services on Kubernetes, including scheduling, resource management, operators, failover, or upgrade patterns.
- Background with HA design, leader election, distributed locking, replication state machines, or recovery workflows.
- Familiarity with Linux kernel internals, VFS, inode structures, POSIX semantics, FUSE, or eBPF.
- Experience with storage benchmarking, workload characterization, structured logging, metrics, tracing, or production debugging.
- Experience using AI-assisted engineering tools for code generation, unit tests, debugging, documentation, log analysis, or workflow automation with appropriate validation.
- Prior ownership of production file system, storage, database, distributed metadata, or protocol-layer components at scale.
- Deep experience with NFSv4, SMBv3, multi-protocol file access, or enterprise storage systems.
- Demonstrated ability to improve reliability, performance, or operability for customer-facing infrastructure.
- Experience mentoring engineers in systems design, concurrency, debugging, testing strategy, and operational excellence.
- Strong AI fluency in engineering workflows, including structured prompting, validation through tests and benchmarks, and sound judgment about when not to use AI-generated output.

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