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 cloud, with exposure to computer vision, speech recognition, and NLP. Key responsibilities include architecting and developing secure, privacy-preserving solutions for continuous model improvement, and building platforms to accelerate research and product development. Requires strong software engineering experience, cloud architecture knowledge, and proficiency in Python/C++ and ML frameworks. Experience with LLMOps and fine-tuning large models is preferred.

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
  • privacy-preserving AI techniques

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 - evaluation, monitoring, quantization, teacher-learner
  • fine-tuning large parameter models
  • model encryption and privacy preserving AI techniques