Software Engineer III (AI)

iXSystems•Campbell, CA

4d•Remote

About The Position

TrueNAS is redefining enterprise storage by delivering proven data resilience, performance, and flexibility without the complexity or constraints of legacy solutions. As the most deployed storage platform in the world, TrueNAS already powers critical data storage infrastructure for businesses, research institutions, and government agencies across 200+ countries and millions of users. Our mission is simple: to become the most trusted name in data storage. Originally founded in Silicon Valley in 2002 under the name iXsystems, TrueNAS is a profitable, independent company with a culture rooted in trust, curiosity, technical excellence, and continuous improvement. Whether you work remotely or on-site, you’ll be part of a team that values collaboration and meaningful impact - where people come before profit, and bold ideas drive the future of data infrastructure. If you’re ready to help shape the future of enterprise storage, we’d love to connect. As a Software Engineer III (AI), you'll build the AI infrastructure that runs natively on TrueNAS, turning a storage platform into an active participant in modern AI workflows, rather than a passive backend. This role typically suits engineers with a few years of varied software engineering experience, often around 3-5 years, and sits at the intersection of ZFS internals and applied ML infrastructure. This is a remote position in the United States.

Requirements

Strong backend engineering fundamentals.
Comfortable in Go, Python, or C and able to pick up others.
Shipped systems that handle real load.
Hands-on experience with vector search, embedding models, or RAG pipelines in production.
Understanding the difference between a demo and something that holds up under scale.
Familiarity with GPU programming or GPU-accelerated libraries (CUDA, cuVS, DALI, TensorRT, or equivalents).
Comfort working close to the storage and filesystem layer.
Experience with Agentic Engineering, including a solid understanding of how to use AI-first development workflows for most common engineering disciplines.
An undergraduate or advanced degree in Computer Science, Computer Engineering, or a related discipline, or comparable work experience.

Nice To Haves

ZFS experience is a strong plus; if not ZFS, then equivalent depth in another filesystem.
Background in time-series anomaly detection or ML-driven observability.
Contributions to open-source storage, ML infrastructure, or MCP-related projects.
Experience with model quantization (4-bit, BitNet) and on-device inference constraints.

Responsibilities

Develop and optimize on-array vectorization pipelines, including systems that generate embeddings for data living on TrueNAS, per-file sidecar generation driven by filesystem events, and snapshot-time index compilation.
Build out GPU-accelerated inference paths using libraries like cuVS/CAGRA, targeting NVIDIA L4/A2-class GPUs in appliance form factors, making decisions about model selection, quantization tradeoffs, and coexistence of inference workloads with storage workloads.
Develop AIOps systems for anomaly detection, predictive maintenance, and log analysis that consume TrueNAS telemetry to provide fleet-scale insights for operators.
Extend MCP surface so TrueNAS systems are first-class citizens in agent-driven workflows, acting as both data sources and systems agents can operate on.