Senior Machine Learning Engineer

Axon Axon · Enterprise · Office, WA · 2014 Artificial Intelligence

Senior Machine Learning Engineer at Axon focused on developing and deploying AI solutions for public safety. The role involves supporting the training, evaluation, testing, and deployment of ML models on devices and in the cloud, working across computer vision, speech recognition, and NLP. Key responsibilities include building POCs, architecting secure and privacy-preserving solutions for model improvement, and developing platforms to accelerate AI development. Requires strong software engineering experience, cloud architecture knowledge, and proficiency in Python/C++ and ML frameworks.

What you'd actually do

  1. Collaborate with scientists and product managers to build proof-of-concepts (POCs) contributing to shaping the Axon of tomorrow.
  2. Architect and develop secure, privacy-preserving, solutions to enable the continuous improvement of existing AI models.
  3. Architect platforms that accelerate research and AI product development.
  4. Collaborate with scientists in architecting and implementing state-of-the-art training techniques.
  5. Set high standards for ethical and responsible AI development.

Skills

Required

  • 6+ years of software engineering experience
  • proven track record of successfully deploying AI models to the cloud
  • Experience with Infrastructure-as-code and cloud architecture
  • Proficiency in Python and C++
  • familiarity with ML frameworks such as TensorFlow, or PyTorch
  • Advanced knowledge and hands-on experience with Linux
  • Excellent problem solving skills
  • ability to dive deep into system architecture
  • Excellent software design skills
  • Comfort communicating and interacting with scientists, engineers and product managers

Nice to have

  • Master’s Degree/PhD in Computer Science, Engineering, Electronics, Mathematics or an equivalent highly technical field
  • Experience with LLMOps - evaluation, monitoring, quantization, teacher-learner, etc.)
  • Hands-on experience with fine-tuning large parameter models
  • Familiarity with model encryption and privacy preserving AI techniques

What the JD emphasized

  • successfully deploying AI models to the cloud
  • LLMOps
  • fine-tuning large parameter models

Other signals

  • deploying AI models to the cloud
  • continuous improvement of existing AI models
  • accelerate research and AI product development
  • implementing state-of-the-art training techniques
  • LLMOps
  • fine-tuning large parameter models