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Axiado is an AI-enhanced security processor company redefining the control and management of every digital system. The company was founded in 2017, and currently has 150+ employees. At Axiado, developing great technology takes more than talent: it takes amazing people who understand collaboration, respect each other, and go the extra mile to achieve exceptional results. It takes people who have the passion and desire to disrupt the status quo, deliver innovation, and change the world. If you have this type of passion, we invite you to apply for this job.

This is a "full-stack" ML systems role for a seniorindividual contributorand 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: 

1. MLOps & Data:You will architect the entire data lifecycle, including our CI/CD pipelines, data-labeling loops, and on-device monitoring. 

1. 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. 

1. 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 customNPU. 

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. 

Key Responsibilities 

Architecture & Leadership: 

• Act as a seniorindividual 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. 

MLOps & Data Platform: 

• Design and implement our data processing and versioning pipelines, ensuring data integrity and traceability. 

• Build the infrastructure for ourHuman-in-the-Loop (HITL)andAI-in-the-Loop (Active Learning)data labeling systems to continuously improve our datasets. 

• Develop a comprehensive, lightweighton-device monitoringsystem to track not just operational metrics but alsoinference qualityand concept drift. 

Agentic & Edge Development: 

• Design and development ofautonomous agentsthat operate on our resource-constrained edge devices. 

• Integrate deep domain knowledge, including real-timelog analysis,computer vision, and interaction with open-source system tools. 

Security & Optimization: 

• Define and implement the completesecurity and verificationframework for our edge models. This includesMCP/A2A-like secure protocols,MCP authentication,entity verification(e.g., model signing), andmodel injection prevention. 

• Serve as the primary technical bridge to our silicon teams.Collaborate with RTL designersto influence futureNPUandFPGAarchitecture from an ML software perspective. 

• Lead R&D on model optimization for our specificAI inference engine, applying bothgraph-level(e.g., operator fusion) andOP-level(e.g., custom ops) techniques. 

• 8-10+ yearsof 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 ofRTL (Verilog/VHDL)in the hardware design lifecycle. 

• Proven experiencearchitecting and building end-to-end MLOpslifecycles, from data ingestion to production monitoring and labeling loops. 

• Proven experience developingagentic systemsor applications using LLMs. 

• Demonstrable domain knowledge inlog analysisAND/ORcomputer vision. 

• Experience withon-device model security(verification, anti-injection) and secure communication protocols. 

• Hands-on experience optimizing models for hardware (NPUs, GPUs) at graph and operator levels. 

Preferred Qualifications (The "Plus" Factors) 

• Direct experience withML compilerssuch asApache TVMor MLIR. 

• Hands-on experience withKubernetes (K8s)for MLOps (e.g., Kubeflow, Argo). 

• Familiarity with GPU scheduling and virtualization platforms likeRun:AI. 

• Deep experience withembedded system development (C++, Rust, Yocto). 

• Familiarity with theRISC-Vinstruction set architecture (ISA). 

• Proficiency with cloud platforms (AWS, GCP, Azure). 

Compensation Range - $80,000 - $200000

Axiado is committed to attracting, developing, and retaining the highest caliber talent in a diverse and multifaceted environment. We are headquartered in the heart of Silicon Valley, with access to the world's leading research, technology and talent.

We are building an exceptional team to secure every node on the internet. For us, solving real-world problems takes precedence over purely theoretical problems. As a result, we prefer individuals with persistence, intelligence and high curiosity over pedigree alone. Working hard and smart, continuous learning and mutual support are all part of who we are.

Axiado is an Equal Opportunity Employer. Axiado does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, non-disqualifying physical or mental disability, national origin, veteran status or any other basis covered by appropriate law. All employment is decided on the basis of qualifications, merit, and business need.