Build the AI infrastructure that runs natively on TrueNAS
Design and implement systems that generate embeddings for data living on TrueNAS
Build out the inference path using cuVS/CAGRA and similar libraries
Develop anomaly detection, predictive maintenance, and log analysis systems
Extend MCP surface so TrueNAS systems are first-class citizens in agent-driven workflows
Requirements
3-5 years of varied software engineering experience
Strong backend engineering fundamentals
Comfortable in Go, Python, or C
Hands-on experience with vector search, embedding models, or RAG pipelines in production
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
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
Undergraduate or advanced degree in Computer Science, Computer Engineering, or a related discipline expected, with comparable work experience as an alternative
Tech Stack
Python
Go
Benefits
health, dental, vision, disability, and life insurance