Senior Machine Learning Scientist

Axon Axon · Enterprise · WA · Remote · 2014 Artificial Intelligence

Seeking a Senior Machine Learning Scientist to join Axon's AI team, focusing on LLM, MLLM, Computer Vision, and GenAI applications in Cloud, Devices, and Robotics. The role involves researching, developing, and deploying cutting-edge models and algorithms, with a focus on enabling intelligent reasoning and perception of multimodal data. Responsibilities include technical leadership, optimizing models for resource-constrained devices, and contributing to the ML development lifecycle.

What you'd actually do

  1. Own one or more key technical areas across LLM, MLLM, CV product portfolio.
  2. Provide technical leadership to junior scientists, guiding the transition of R&D concepts into impactful Axon product feature.
  3. Research and develop cutting-edge techniques in LLM, MLLMs, GenAI, and Computer Vision across cloud, devices and sensors based data sources.
  4. Design and implement efficient and scalable MLLM models for inference and analysis of multimodal data.
  5. Explore novel approaches to address challenges in NLP, NLU, Object Detection, Object Recognition, Object Tracking, Segmentation, and Scene Understanding.

Skills

Required

  • PhD and with +5 years for ML Scientist, +8 years for Sr. ML Scientist, +10 years for Principal ML Scientist experience in Computer Science or a related field with a focus on LLM, MLLMs, Computer Vision, GenAI.
  • Python
  • C/C++
  • TensorFlow
  • PyTorch
  • Keras
  • ROS or robotic operational system

Nice to have

  • Experience in developing computer vision algorithms for resource-constrained devices such as mobile phones, IoT devices, or embedded systems is highly desirable.
  • Experience with hardware accelerators and optimize algorithms for specific hardware architectures.

What the JD emphasized

  • Proven track record of research excellence in LLM, MLLM, Computer Vision, Robotics Perception, GenAI, demonstrated through publications in top-tier conferences or journals.
  • Drive one or more phases of the ML development lifecycle: shape datasets, investigate modeling approaches and architectures, train/evaluate/tune models and implement the end-to-end training pipeline.

Other signals

  • LLM
  • MLLM
  • Computer Vision
  • GenAI
  • Robotics