Your Impact
As a Staff Embedded Software Engineer, you will lead critical software engineering initiatives and set the strategic technical direction across multiple embedded product lines, including body-worn cameras, in-car cameras, stationary cameras, drones, and emerging connected device solutions. Your role involves defining and significantly advancing embedded software architectures and ensuring system-wide excellence in stability, scalability, security, and performance.
You will proactively identify technical opportunities and risks, guiding architectural decisions to future-proof our products against complex operational environments. Your strategic oversight will involve collaboration with executives, directors, managers and cross-functional teams, deeply influencing Axon’s broader software engineering organization. Your mentorship will uplift engineers across multiple teams, driving Axon’s mission-critical standards and technical excellence.
In this role, you will also shape Axon’s approach to applied artificial intelligence on connected devices, influencing how AI models are trained, deployed, evaluated, and deployed across the device and cloud ecosystem. You will help define scalable and responsible AI architectures that balance model performance, operational constraints, safety, and real-world reliability, ensuring AI-driven capabilities can be trusted in mission-critical environments.
What You’ll Do
- 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.
- 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.
- 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.
- Establish and enforce Axon-wide standards for embedded software and AI system design, including reliability, scalability, safety, observability, and lifecycle management.
- 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.
- Provide executive-level guidance and mentorship, significantly enhancing the capabilities and technical decision-making of the embedded software engineering teams.
- Continuously improve software engineering practices and drive excellence through strategic retrospectives, planning sessions, and innovation cycles.
What You Bring
- 12+ years of professional software development experience, with extensive expertise in C/C++, Go, Python, or comparable systems programming languages, including significant experience building AI- and data-intensive systems.
- Deep, demonstrated expertise in embedded systems architecture, firmware integration, and device-level software engineering, combined with 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, including ownership of data pipelines, model evaluation, and iterative improvement in production environments.
- Practical experience with large-scale AI systems, including foundation models and LLMs, such as fine-tuning, adaptation, or integration into real-world products.
- 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, including setting technical direction for AI-enabled products.
- Advanced understanding of computer science fundamentals, data structures, algorithms, and high-standard software design practices, applied to both embedded and large-scale AI systems.
- Experience with networking and distributed system concepts relevant to connected and AI-enabled devices.