Sr. Full Stack Member of Technical Staff

Location: Finland

Join Axon and be a Force for Good.

At Axon, we're on a mission to Protect Life. We're explorers, pursuing society's most critical safety and justice issues with our ecosystem of devices and cloud software. Like our products, we work better together. We connect with candor and care, seeking out diverse perspectives from our customers, communities and each other.

Life at Axon is fast-paced, challenging and meaningful. Here, you'll take ownership and drive real change. Constantly grow as you work hard for a mission that matters at a company where you matter.

Your Impact

We are seeking multiple highly skilled Full-Stack members of Technical Staff (MTS) to join our CoreAI organization, driving end-to-end development of AI systems across Cloud, Edge Devices, Mission Critical and Robotics platforms.

This role goes beyond pure research. You will operate across the full stack, from data, models, and infrastructure to system integration and production deployment, to deliver scalable, real-world AI solutions. You will work on cutting-edge applications spanning computer vision, NLU, multimodal AI (MLLMs), and GenAI, enabling intelligent perception, reasoning, and action in mission-critical environments.

As a senior technical leader, you will partner closely with research scientists, machine learning engineers, hardware, firmware, and product teams to translate advanced algorithms into robust, production-ready systems that operate reliably at scale.

Why This Role

You will have the opportunity to own and shape end-to-end AI systems that operate in real-world environments, bridging the gap between cutting-edge research and impactful production deployment. This role is ideal for engineers who thrive at the intersection of AI, systems, and product, and want to build scalable, high-impact solutions across cloud and edge.

What You'll Do

Location:

any cities with Axon Engineering Hub in US, Vietnam, EU (see )

* EU:
Finland, Belgium, UK

Responsibilities

* Own end-to-end system development across data pipelines, model development, evaluation, and deployment for AI-powered products.

* Design and build scalable, production-grade systems for real-time perception, multimodal understanding, and decision-making.

* Develop and deploy models across cloud and edge environments, including resource-constrained devices.

* Architect and optimize full-stack AI pipelines, including:

* Translate research advances in computer vision, NLU, GenAI, and multimodal LLMs into production systems.

* Collaborate cross-functionally with MLE, hardware, firmware, and product teams to integrate AI into real-world systems.

* Optimize solutions for hardware-specific acceleration (GPU, NPU, DSP) and edge deployment.

* Define and drive evaluation frameworks and metrics to ensure system performance, reliability, and safety.

* Lead technical direction for complex, ambiguous problems and influence system architecture across teams.

* Contribute to technical design docs, internal knowledge sharing, and long-term platform strategy.

Basic Qualifications

* Master's, or PhD in Computer Science, Electrical Engineering, or related field

* 8+ years (L8) or 10+ years (L9) of industry experience building production systems

* Strong programming skills in Python/C++ and experience with modern ML frameworks (e.g., PyTorch, Tensor Flow)

* Proven experience delivering end-to-end systems from concept to production

* Deep expertise in one or more areas:

* Experience building and scaling distributed systems or cloud-based ML pipelines

* Experience optimizing models for latency, cost, and deployment constraints

Preferred Qualifications

* Experience deploying AI both on Cloud (AWS, Azure

ML), edge devices (e.g., mobile, embedded systems, robotics platforms on Qualcomm, NVIDIA, Intel)

* Familiarity with hardware acceleration frameworks (Tensor

RT, ONNX, SNPE, etc.)

* Experience with real-time systems and streaming data pipelines

* Knowledge of multimodal data processing (vision, audio, text, sensor fusion)

* Experience with AWS or other cloud platforms for large-scale inference and training

* Strong system design and architecture skills across cloud edge environments

* Track record of technical leadership,…