Staff ML Engineer

About the role

Were looking for an ML engineer who works across the full stack from model to silicon comfortable optimizing training and inference performance on GPU/AI-accelerator infrastructure building or tuning models and adapting model and inference-engine design to the constraints of the underlying chip and its NPU. Youll move fluidly between algorithm work systems-level software and infrastructure work closing the loop end-to-end rather than owning just one layer of the stack. This is a rare chance to work the full cycle of AI silicon from model down to chip something most ML engineers at large companies never get access to.

What youll do

• Optimize training and inference performance across GPU and AI-accelerator infrastructure including MLOps pipelines
• Design train and evaluate ML models (deep learning LLM CV or recommendation systems) and take them into production
• Harden and extend NPU cores (e.g. building on an open RVV/tensor core like CoralNPU) into production silicon
• Build or optimize inference engines and serving runtimes against real hardware constraints latency memory and power
• Work below the application layer where needed BMC firmware embedded Linux or RTOS (e.g. Zephyr) so AI features run reliably on real systems
• Build automated test/verification harnesses that close the loop for AI-assisted RTL/DV hardware bring-up or manufacturing test
• Apply ML to security AI-driven log/intrusion analysis AI-assisted penetration testing or firmware/hardware security work
• Collaborate closely with RTL/hardware firmware and QA teams to ship AI features end-to-end from training through deployment and monitoring

Qualifications :

What were looking for

• 57 years of hands-on AI/ML experience; Masters required PhD preferred
• Hands-on experience with AI/ML infrastructure and performance GPU clusters distributed training inference-serving optimization MLOps pipelines
• Model / algorithm development experience designing training and evaluating ML models
• Experience taking models into production feature engineering data pipelines deployment
• AI chip / hardware-aware ML experience optimizing inference engines for a specific chip or adapting model architecture/quantization to chip constraints
• Deep hands-on expertise in at least 2 of the following 5 specialty areas we dont expect all five:

NPU / AI-accelerator hardening or extending an NPU core into production silicon mapping models onto MAC/tensor-engine constraints or NPU-aware RTL/DV work

Systems / Sys-level software BMC firmware embedded Linux RTOS (e.g. Zephyr) or other low-level system software

Inference engine / runtime built or materially optimized an inference engine or serving runtime against real hardware constraints

Test / verification harness built an automated harness that closes a loop e.g. an agent-driven RTL/DV test runner or a hardware bring-up / MFG test harness

Cyber security AI-driven log/intrusion analysis AI-assisted penetration testing or firmware/hardware security

Additional Information :

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 worlds 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.

Remote Work :

No

Employment Type :

Full-time