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 data pipelines, model development, evaluation, and deployment for AI-powered products, focusing on computer vision, NLU, multimodal AI, and GenAI. The engineer will translate research into production systems, optimize for cloud and edge, and ensure system performance, reliability, and safety.

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
  • end-to-end systems delivery
  • distributed systems or cloud-based ML pipelines
  • optimizing models for latency, cost, and deployment constraints

Nice to have

  • AWS, AzureML
  • edge devices (mobile, embedded systems, robotics platforms on Qualcomm, NVIDIA, Intel)
  • TensorRT, ONNX, SNPE
  • 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
  • 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
  • AI both on Cloud (AWS, AzureML), edge devices
  • real-time systems
  • 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