Sr. Full Stack Member of Technical Staff

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

Senior 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. The 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. It requires translating research into production-ready systems, optimizing for cloud and edge, and defining 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++
  • 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
  • own and shape end-to-end AI systems
  • impactful production deployment
  • build scalable, high-impact solutions
  • end-to-end system development
  • production-grade systems
  • real-time perception
  • multimodal understanding
  • decision-making
  • deploy models across cloud and edge environments
  • resource-constrained devices
  • full-stack AI pipelines
  • production systems
  • real-world systems
  • hardware-specific acceleration
  • edge deployment
  • evaluation frameworks
  • system performance, reliability, and safety
  • technical direction for complex, ambiguous problems
  • system architecture across teams
  • technical design docs
  • long-term platform strategy
  • building production systems
  • delivering end-to-end systems from concept to production
  • scaling distributed systems
  • cloud-based ML pipelines
  • optimizing models for latency, cost, and deployment constraints
  • deploying AI both on Cloud
  • edge devices
  • robotics platforms
  • hardware acceleration frameworks
  • real-time systems
  • streaming data pipelines
  • multimodal data processing
  • large-scale inference and training
  • system design and architecture skills
  • cloud ↔ edge environments
  • technical leadership
  • 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