Senior Software Engineer II (TASER Data Science)

Axon•Seattle, WA

3h•Hybrid

About The Position

Join Axon and be a Force for Good. At Axon, we’re on a mission to Protect Life. We’re explorers, pursuing society’s most critical safety and justice issues with our ecosystem of devices and cloud software. Like our products, we work better together. We connect with candor and care, seeking out diverse perspectives from our customers, communities and each other. Life at Axon is fast-paced, challenging and meaningful. Here, you’ll take ownership and drive real change. Constantly grow as you work hard for a mission that matters at a company where you matter. Your Impact Law enforcement agencies need training systems informed by real data about how officers perform in the field. We've spent the last few years collecting that data and validating the data products that create actionable insights that trainers can use to make encounters more effective, less injurious, and safer for everyone involved. We've shipped the simple models, but now we need to get our customer-validated models into production. Your job is not just to ship them, but to ensure that we have a strategic pipeline to continue delivering data products for our customers for years to come. What works in research needs to be production-grade, reliable, and reusable. We're a small, technically deep team embedded in Axon's TASER pillar. Our work spans hardware telemetry and behavioral science — we analyze TASER device data, build models that drive training recommendations for law enforcement agencies, and ship the analytical tools that get those insights to the people who can act on them. Our analyses reach the C-suite. Our models become user-facing features. We're working toward Axon's Moonshot: reduce fatal officer-involved shootings by 50% in the next decade. The head of product was a Staff Engineer. Our TPM ran a 60-person engineering organization. Both product managers have quantitative degrees — applied mathematics and engineering. Our program manager for TREND is a former state police lieutenant with over 30 years of experience in law enforcement. Our designer is part of the team, not separated from it. We built this team to cover the full space — engineering, data, product, and domain — and this hire is the next deliberate addition. This is the opposite of a silo — and it comes with a tradeoff: you won't always have another SWE in the room, so you'll need to be technically self-sufficient. What you get in return is a team with more depth of experience per person than most engineering environments you've worked in.

Requirements

You write production code at a high standard — strongly typed, comprehensively tested, designed for the people who will maintain it after you. We write Python like software engineers, not data scientists.
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

Nice To Haves

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." We hire for intellectual diversity because it makes the work better.
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

Responsibilities

Build and ship data products: dashboards, metrics systems, and recommendation tools that drive real decisions
Own production ML deployment — bring models from research to reliable production systems with monitoring, versioning, and operational rigor
Build and own data pipelines from TASER device telemetry through to analytics surfaces used by agencies and internal stakeholders
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
Work across the full stack — device-side data ingestion through user-facing analytics — and move between projects to build breadth
Use AI tools as a core part of your development workflow, not a novelty