Staff AI Embedded Software Engineer - Connected Devices

Axon Axon · Enterprise · Office, WA · 2004 Sensors - Devices

Staff Embedded Software Engineer focused on leading AI initiatives for connected devices, including on-device inference and cloud integration. The role involves defining embedded software architectures, leading AI-enabled capabilities, integrating emerging AI approaches (foundation models, multimodal systems), and establishing standards for AI systems. Requires deep expertise in embedded systems, AI model deployment on edge platforms, and experience with large-scale AI systems.

What you'd actually do

  1. Define and significantly advance embedded software architectures for Axon’s current and future connected device products, including AI-enabled systems spanning on-device inference and cloud-assisted workflows.
  2. Lead the technical direction for AI-enabled capabilities across connected devices, including collaboration on large-scale model training, data strategy, deployment, and iterative improvement in production, across multiple product lines.
  3. Partner with research, product, and platform teams to explore and integrate emerging AI approaches, including foundation models and multimodal systems, shaping Axon’s medium and long-term AI strategy for connected devices.
  4. Establish and enforce Axon-wide standards for embedded software and AI system design, including reliability, scalability, safety, observability, and lifecycle management.
  5. Identify and mitigate risks associated with AI systems, including model failure modes, data drift, and operational edge cases, and drive architectural decisions that ensure safe and reliable behavior in real-world conditions.

Skills

Required

  • 12+ years of professional software development experience
  • Extensive expertise in C/C++, Go, Python, or comparable systems programming languages
  • Significant experience building AI- and data-intensive systems
  • Deep, demonstrated expertise in embedded systems architecture, firmware integration, and device-level software engineering
  • Hands-on experience deploying and optimizing AI inference workloads on constrained edge platforms (MCUs, SoCs, NPUs)
  • Proven experience designing, training, and operating machine learning models at scale
  • Ownership of data pipelines, model evaluation, and iterative improvement in production environments
  • Practical experience with large-scale AI systems, including foundation models and LLMs
  • Proven track record of addressing and resolving system-wide challenges in performance, scalability, reliability, security, and safety across AI-enabled and mission-critical systems
  • At least 7+ years mentoring senior engineers and leading complex, strategic engineering initiatives across multiple teams
  • Setting technical direction for AI-enabled products
  • Advanced understanding of computer science fundamentals, data structures, algorithms, and high-standard software design practices
  • Experience with networking and distributed system concepts relevant to connected and AI-enabled devices

What the JD emphasized

  • AI-enabled systems
  • on-device inference
  • cloud-assisted workflows
  • large-scale model training
  • foundation models
  • multimodal systems
  • AI systems
  • mission-critical environments
  • AI-driven capabilities

Other signals

  • AI on connected devices
  • on-device inference
  • cloud-assisted workflows
  • foundation models
  • multimodal systems
  • responsible AI architectures