Senior Software Engineer II (TASER Data Science)
HybridScottsdale, Arizona, United States
Job Summary
Build and ship data products, dashboards, and recommendation tools that drive real decisions for law enforcement agencies. Own production ML deployment by bringing models from research to reliable systems with monitoring, versioning, and operational rigor. Build and own data pipelines from TASER device telemetry through to analytics surfaces used by internal stakeholders and customers. Set technical direction for engineering practices and work across the full stack, from device-side data ingestion to user-facing analytics. Use AI tools as a core part of your development workflow. This role is based in Seattle or Scottsdale with a hybrid schedule (Tuesdays through Fridays onsite).
Required Qualifications
- You write production code at a high standard — strongly typed, comprehensively tested, designed for the people who will maintain it after you
- You've deployed and operated ML systems in production: model serving, monitoring, failure handling, and the operational rigor that keeps them running
- You identify the most important technical work and go after it — you've shaped technical roadmaps, influenced peers and organizational direction, and moved goals forward with or without explicit direction
- You define the problem as much as you solve it — you thrive in a team where requirements evolve as you learn, and you see that as a feature, not a bug
- You've worked with real-world messy data: device logs, behavioral data, event streams, or similar
- Must-haves
Desired Qualifications
- Advanced degree in a quantitative or analytical field — PhDs are very welcome, we already have three
- Intellectual background outside computer science is genuinely valued here: statistics, physics, engineering, biology, economics, linguistics, philosophy — it doesn't have to be a "hard science"
- Hands-on experience with ML production tooling: model registry, serving infrastructure, pipeline orchestration, and model monitoring
- Experience with cloud data platforms in an ML context (Azure ML, Databricks, Snowflake) and batch or streaming pipeline architecture
- Experience with hardware-adjacent data: device telemetry, IoT event logs, or similar
- Strong preferences
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