Software Engineer III (AI)

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