Senior Software Engineer II (taser Data Science)

Axon Axon · Enterprise · Office, WA · 2003 De-escalation Devices

Senior Software Engineer II role focused on building and shipping data products, owning production ML deployment, and establishing technical direction for engineering practices within the TASER Data Science team. The role involves bringing models from research to reliable production systems with operational rigor and building data pipelines from device telemetry to analytics surfaces.

What you'd actually do

  1. Build and ship data products: dashboards, metrics systems, and recommendation tools that drive real decisions
  2. Own production ML deployment — bring models from research to reliable production systems with monitoring, versioning, and operational rigor
  3. Build and own data pipelines from TASER device telemetry through to analytics surfaces used by agencies and internal stakeholders
  4. Set technical direction for the team's engineering practices — the data scientists here write code and want to do it better; you'll be the senior engineering voice they've been missing
  5. Work across the full stack — device-side data ingestion through user-facing analytics — and move between projects to build breadth

Skills

Required

  • production code
  • Python
  • deployed and operated ML systems in production
  • model serving
  • monitoring
  • failure handling
  • technical roadmaps
  • real-world messy data
  • device logs
  • behavioral data
  • event streams

Nice to have

  • ML production tooling
  • model registry
  • pipeline orchestration
  • model monitoring
  • cloud data platforms
  • Azure ML
  • Databricks
  • Snowflake
  • batch or streaming pipeline architecture
  • hardware-adjacent data
  • device telemetry
  • IoT event logs

What the JD emphasized

  • production ML deployment
  • reliable production systems
  • operational rigor
  • production code at a high standard
  • deployed and operated ML systems in production

Other signals

  • production ML deployment
  • bring models from research to reliable production systems
  • operational rigor
  • technical direction for engineering practices
  • data pipelines