Sr. Full Stack Member of Technical Staff

Axon Axon · Enterprise · WA · Remote · 2014 Artificial Intelligence

Full-Stack Member of Technical Staff to join CoreAI, driving end-to-end development of AI systems across Cloud, Edge Devices, Mission Critical and Robotics platforms. This role operates across the full stack, from data, models, and infrastructure to system integration and production deployment, to deliver scalable, real-world AI solutions. Work on cutting-edge applications spanning computer vision, NLU, multimodal AI (MLLMs), and GenAI, enabling intelligent perception, reasoning, and action in mission-critical environments.

What you'd actually do

  1. Own end-to-end system development across data pipelines, model development, evaluation, and deployment for AI-powered products.
  2. Design and build scalable, production-grade systems for real-time perception, multimodal understanding, and decision-making.
  3. Develop and deploy models across cloud and edge environments, including resource-constrained devices.
  4. Architect and optimize full-stack AI pipelines, including:
  5. Translate research advances in computer vision, NLU, GenAI, and multimodal LLMs into production systems.

Skills

Required

  • Python/C++
  • modern ML frameworks (e.g., PyTorch, TensorFlow)
  • delivering end-to-end systems from concept to production
  • building and scaling distributed systems or cloud-based ML pipelines
  • optimizing models for latency, cost, and deployment constraints

Nice to have

  • Experience deploying AI both on Cloud (AWS, AzureML), edge devices (e.g., mobile, embedded systems, robotics platforms on Qualcomm, NVIDIA, Intel)
  • Familiarity with hardware acceleration frameworks (TensorRT, 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, mentoring, and driving cross-team initiatives
  • Experience in privacy-preserving AI, security, or safety-critical systems

What the JD emphasized

  • end-to-end development
  • production deployment
  • scalable, real-world AI solutions
  • mission-critical environments
  • robust, production-ready systems
  • operate reliably at scale
  • end-to-end AI systems
  • impactful production deployment
  • build scalable, high-impact solutions
  • end-to-end system development
  • production-grade systems
  • Develop and deploy models
  • production systems
  • production
  • scaling
  • production
  • large-scale inference
  • system design and architecture
  • safety-critical systems

Other signals

  • end-to-end AI systems
  • cloud and edge deployment
  • mission-critical environments
  • computer vision, NLU, multimodal AI, GenAI