Sr. Full Stack Member of Technical Staff

Axon Axon · Enterprise · Finland · Remote · 2014 Artificial Intelligence

Full-stack engineer to drive end-to-end development of AI systems across Cloud, Edge Devices, Mission Critical and Robotics platforms. This role involves full-stack development from data, models, and infrastructure to system integration and production deployment, focusing on computer vision, NLU, multimodal AI, and GenAI applications. The engineer will translate research into production-ready systems, optimize for cloud and edge, and define evaluation frameworks.

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++
  • PyTorch, TensorFlow
  • delivering end-to-end systems
  • 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)
  • hardware acceleration frameworks (TensorRT, ONNX, SNPE, etc.)
  • real-time systems and streaming data pipelines
  • multimodal data processing (vision, audio, text, sensor fusion)
  • AWS or other cloud platforms for large-scale inference and training
  • Strong system design and architecture skills across cloud ↔ edge environments
  • technical leadership, mentoring, and driving cross-team initiatives
  • privacy-preserving AI, security, or safety-critical systems

What the JD emphasized

  • end-to-end development
  • production deployment
  • scalable, real-world AI solutions
  • cloud and edge
  • production-grade systems
  • resource-constrained devices
  • production systems
  • 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 and streaming data pipelines
  • system design and architecture skills across cloud ↔ edge environments
  • safety-critical systems

Other signals

  • end-to-end AI systems
  • cloud and edge deployment
  • computer vision, NLU, multimodal AI, GenAI
  • production deployment
  • scalable, high-impact solutions