Senior Machine Learning Engineer II

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

Senior Machine Learning Engineer to join a new team building agentic video and multimodal reasoning systems. The role involves owning the systems and infrastructure that turn research into production-ready AI capabilities, including data pipelines, training/evaluation infrastructure, inference/serving, and retrieval/orchestration for agentic reasoning over video and multimodal data.

What you'd actually do

  1. Own end to end the systems and infrastructure that take multimodal and video models from research prototype to reliable, scalable production.
  2. Design and operate data and ingest pipelines for large-scale video and multimodal corpora — storage, processing, labeling, and retrieval.
  3. Build and scale training and evaluation infrastructure, and turn scientists’ evaluation methodology into automated, reproducible measurement systems.
  4. Build inference and serving systems for real-time and batch multimodal workloads, optimizing latency, throughput, and cost.
  5. Build the retrieval, indexing, and embedding infrastructure (vector search at scale) and the orchestration and tool-use plumbing behind agentic reasoning over video and multimodal data.

Skills

Required

  • Python
  • ML frameworks such as PyTorch or TensorFlow
  • cloud infrastructure at scale (AWS, GCP, or Azure)
  • distributed systems
  • container orchestration
  • large-scale distributed platforms
  • ML lifecycle systems
  • model serving and inference at scale
  • production monitoring
  • large-scale data systems handling video or other high-volume, high-dimensional multimodal data

Nice to have

  • Master’s or PhD in Computer Science, Engineering, or an equivalent highly technical field
  • video processing or video understanding pipelines at scale
  • retrieval systems
  • vector search
  • RAG infrastructure
  • serving foundation models
  • agentic systems
  • LLM tool-use orchestration
  • responsible AI
  • de-identification
  • privacy-preserving techniques

What the JD emphasized

  • 10+ years of software engineering experience
  • proven track record of architecting, operating, and maintaining large-scale distributed platforms in production
  • Deep experience building systems across the ML lifecycle: data pipelines, training and evaluation infrastructure, model serving and inference at scale, and production monitoring
  • Experience with large-scale data systems handling video or other high-volume, high-dimensional multimodal data
  • Hands-on experience operating cloud infrastructure at scale (AWS, GCP, or Azure), including distributed systems and container orchestration

Other signals

  • building agentic video and multimodal reasoning systems
  • own the systems and infrastructure that turn cutting-edge research into safe, reliable, and scalable AI capabilities
  • build everything that stands between a promising model and a production system operating on video and multimodal data at scale