Senior ML Engineer

Axiado is an AI-enhanced security processor company redefining the control and management of every digital system. Axiado builds silicon-rooted security and management chips — including the TCU (Trusted Control/Compute Unit) — for AI data center infrastructure, combining platform security, BMC/firmware, and on-chip AI for real-time threat detection and dynamic power/thermal management. The company closed an oversubscribed $100M+ Series C+ round in December 2025 (led by Maverick Silicon), on top of an earlier $60M Series C — well-funded and scaling fast.

The company was founded in 2017, and currently has 100+ 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.

ABOUT THE ROLE

We're 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. You'll 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.

• Hands-on experience in at least 1 of the following 5 specialty areas

– 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

Responsibilities

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; Work on at least one specialty area in depth — NPU hardening, systems-level software, inference engines, test/verification harnesses, or security-focused ML (see below); Build or optimize inference engines and serving runtimes against real hardware constraints — latency, memory, and power; Collaborate closely with RTL/hardware, firmware, and QA teams to ship AI features end-to-end, from training through deployment and monitoring

Requirements

3–5+ years of hands-on AI/ML experience, Bachelor's required, Master's 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