Staff Machine Learning Engineer

Axiado•San Jose, CA

1d•$80,000 - $200,000•Onsite

About The Position

This is a "full-stack" ML systems role for a senior individual contributor and technical architect. You will be responsible for designing the complete ML ecosystem for our edge devices, from the cloud-native MLOps platform down to the bare-metal model optimization. This unique role blends three key domains: MLOps & Data: You will architect the entire data lifecycle, including our CI/CD pipelines, data-labeling loops, and on-device monitoring. Agentic & Edge AI: You will lead the design of autonomous agents that run on our edge devices, using domain knowledge in log analysis and computer vision. Systems & Hardware: You will be the "hardware-aware" expert, bridging our ML software with our silicon team to ensure our models are hyper-optimized for our custom NPU. You are the engineer who will not only build our ML platform but also design the intelligent agents it deploys and ensure they run faster and more securely than anyone else's.

Requirements

8-10+ years of hands-on experience in machine learning, with a proven track record as a senior or staff-level individual contributor.
Ph.D. or M.S. in Computer Science, Electrical Engineering, or a related field (or equivalent practical experience).
Expert-level programming in Python and deep experience with ML frameworks (e.g., PyTorch, TensorFlow).
Deep theoretical understanding of modern ML algorithms (e.g., Transformers).
A strong foundational understanding of computer architecture, digital logic, and the role of RTL (Verilog/VHDL) in the hardware design lifecycle.
Proven experience architecting and building end-to-end MLOps lifecycles, from data ingestion to production monitoring and labeling loops.
Proven experience developing agentic systems or applications using LLMs.
Demonstrable domain knowledge in log analysis AND/OR computer vision.
Experience with on-device model security (verification, anti-injection) and secure communication protocols.
Hands-on experience optimizing models for hardware (NPUs, GPUs) at graph and operator levels.

Nice To Haves

Direct experience with ML compilers such as Apache TVM or MLIR.
Hands-on experience with Kubernetes (K8s) for MLOps (e.g., Kubeflow, Argo).
Familiarity with GPU scheduling and virtualization platforms like Run:AI.
Deep experience with embedded system development (C++, Rust, Yocto).
Familiarity with the RISC-V instruction set architecture (ISA).
Proficiency with cloud platforms (AWS, GCP, Azure).

Responsibilities

Act as a senior individual contributor, leading by example with hands-on coding, design, and analysis across the entire ML stack.
Define the end-to-end architecture for our MLOps, agentic AI, and model optimization strategy.
Design and implement our data processing and versioning pipelines, ensuring data integrity and traceability.
Build the infrastructure for our Human-in-the-Loop (HITL) and AI-in-the-Loop (Active Learning) data labeling systems to continuously improve our datasets.
Develop a comprehensive, lightweight on-device monitoring system to track not just operational metrics but also inference quality and concept drift.
Design and development of autonomous agents that operate on our resource-constrained edge devices.
Integrate deep domain knowledge, including real-time log analysis, computer vision, and interaction with open-source system tools.
Define and implement the complete security and verification framework for our edge models. This includes MCP/A2A-like secure protocols, MCP authentication, entity verification (e.g., model signing), and model injection prevention.
Serve as the primary technical bridge to our silicon teams. Collaborate with RTL designers to influence future NPU and FPGA architecture from an ML software perspective.
Lead R&D on model optimization for our specific AI inference engine, applying both graph-level (e.g., operator fusion) and OP-level (e.g., custom ops) techniques.