TrueNAS is redefining enterprise storage by delivering proven data resilience, performance, and flexibility. As a Software Engineer III (AI), you'll build the AI infrastructure that runs natively on TrueNAS, transforming it into an active participant in modern AI workflows.
Responsibilities:
- On-array vectorization pipelines. Design and implement the systems that generate embeddings for data living on TrueNAS - including per-file sidecar generation driven by filesystem events and snapshot-time index compilation
- GPU-accelerated inference. Build out the inference path using cuVS/CAGRA and similar libraries, targeting NVIDIA L4/A2-class GPUs in appliance form factors. You'll make decisions about model selection, quantization tradeoffs, and how inference workloads coexist with storage workloads on the same hardware
- AIOps. Develop anomaly detection, predictive maintenance, and log analysis systems that consume the telemetry TrueNAS already produces. The goal is fleet-scale insight that's actually useful to operators, not dashboards full of noise
- MCP and adjacent integrations. Extend our MCP surface so TrueNAS systems are first-class citizens in agent-driven workflows - both as data sources and as systems agents can operate on
Requirements:
- Experienced
- Strong backend engineering fundamentals. You're comfortable in Go, Python, or C and can pick up the others. You've shipped systems that handle real load
- Hands-on experience with vector search, embedding models, or RAG pipelines in production. You understand 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. ZFS experience is a strong plus; if not ZFS, then equivalent depth in another filesystem
- Experience with Agentic Engineering. You should have a solid understanding of how to use AI-first development workflows for most common engineering disciplines
- 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
- An undergraduate or advanced degree in Computer Science, Computer Engineering, or a related discipline is expected, with comparable work experience as an alternative
- Collaboration
- Communication
- Personal Effectiveness
- Delivery
- Problem Solving
- Strategic Thinking
- Analytical Thinking