Sr. Full Stack Member of Technical Staff

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

This role focuses on the end-to-end development and production deployment of AI systems across cloud, edge devices, mission-critical platforms, and robotics. It involves translating research into scalable, real-world AI solutions, working with computer vision, NLU, multimodal AI, and GenAI, and optimizing models for both cloud and resource-constrained edge environments. The role requires strong system design and architecture skills across cloud and edge.

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
  • cloud and edge
  • resource-constrained devices
  • real-time perception
  • multimodal understanding
  • decision-making
  • hardware-specific acceleration
  • edge deployment
  • system performance, reliability, and safety
  • complex, ambiguous problems
  • system architecture
  • end-to-end systems from concept to production
  • deploying AI both on Cloud (AWS, AzureML), edge devices (e.g., mobile, embedded systems, robotics platforms on Qualcomm, NVIDIA, Intel)
  • real-time systems
  • streaming data pipelines
  • multimodal data processing (vision, audio, text, sensor fusion)
  • large-scale inference and training
  • system design and architecture skills across cloud ↔ edge environments
  • safety-critical systems

Other signals

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