Staff Machine Learning Engineer
Core
Designing the complete ML ecosystem for edge devices, from cloud-native MLOps platforms to bare-metal model optimization for custom NPUs.
Role type
Staff Machine Learning Engineer (Edge AI & MLOps Architect)
Builds
Cloud-native MLOps platforms, autonomous agents for edge devices, and optimized inference engines for custom silicon.
Domain
Edge AI, MLOps, Hardware-Aware ML, and Semiconductor Security
Deliverable
production ML models
Required skills
End-to-end MLOps architecture, autonomous agent design, hardware-aware model optimization, computer architecture and RTL understanding, Python programming, ML framework expertise, log analysis, computer vision, on-device model security, graph-level and operator-level optimization
Preferred skills
ML compilers (Apache TVM, MLIR), Kubernetes for MLOps, embedded system development (C++, Rust), RISC-V ISA, cloud platforms (AWS, GCP, Azure)
Technologies
PyTorch, TensorFlow, Apache TVM, MLIR, Kubernetes, Kubeflow, Argo, C++, Rust, Verilog, VHDL, AWS, GCP, Azure
Responsibilities
Define end-to-end architecture for MLOps, agentic AI, and model optimization; design and implement data processing and versioning pipelines; build infrastructure for Human-in-the-Loop and AI-in-the-Loop data labeling systems; develop on-device monitoring systems for inference quality and concept drift; design and develop autonomous agents for resource-constrained edge devices; define and implement security and verification frameworks for edge models; collaborate with RTL designers to influence NPU and FPGA architecture; lead R&D on model optimization for specific AI inference engines
Seniority
Staff, hands-on IC and technical architect
