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 developing data pipelines for TASER device telemetry. The role involves bringing models from research to reliable production systems with operational rigor, setting technical direction for engineering practices, and working across the full stack. Experience with ML production tooling and cloud data platforms is preferred.

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 development (Python)
  • ML system deployment and operation
  • Technical leadership and roadmap influence
  • Problem definition and problem-solving in evolving requirements
  • Experience with real-world messy data (device logs, behavioral data, event streams)

Nice to have

  • Advanced degree in a quantitative or analytical field
  • ML production tooling (model registry, serving infrastructure, pipeline orchestration, model monitoring)
  • Cloud data platforms (Azure ML, Databricks, Snowflake) for ML
  • Batch or streaming pipeline architecture
  • Hardware-adjacent data experience (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

  • Bring models from research to reliable production systems
  • Own production ML deployment
  • Build and ship data products