Senior Software Engineer I

Axon Axon · Enterprise · London, United Kingdom · Axon EVG_R&D

Senior Software Engineer role focused on building a new, AI-native product from concept to launch within Axon's Global Artificial Intelligence pillar. The role requires leading product development, making architectural decisions, and ensuring scalability and quality in a 0->1 environment.

What you'd actually do

  1. Lead the build of major product surfaces and services; own features end-to-end from design to operations.
  2. Drive product clarity in ambiguity: define the problem, shape MVP scope, set success metrics, and align stakeholders on what “done” means.
  3. Build and ship software AI-natively across the SDLC (planning, implementation, testing, code review, debugging, and operations), with clear validation practices to maintain correctness and quality.
  4. Measure and improve delivery outcomes using engineering metrics (lead time, PR throughput, defect rates, test coverage, incident load) and iterate on how the team ships.
  5. Make architecture choices that balance speed and scalability—knowing when “temporary” becomes “permanent.”

Skills

Required

  • 7+ years of software development experience, including ownership of product capabilities in production.
  • Strong engineering judgement under uncertainty; ability to simplify and still build something great.
  • Experience building cloud-native services and/or modern user experiences that scale.
  • Comfort working across boundaries: product, design, customers, and platform dependencies.
  • Bachelor’s degree or equivalent experience.

Nice to have

  • Startup or internal incubation experience.
  • Experience integrating AI/LLM capabilities into product experiences responsibly.
  • Track record of accelerating teams through strong technical leadership.
  • Experience operating in PM-light environments where engineers help drive prioritisation and discovery alongside delivery.

What the JD emphasized

  • Demonstrated, heavy AI-native delivery: consistent use of AI across the SDLC with concrete examples of faster delivery and maintained/improved quality.
  • Product judgement sufficient to ship: ability to frame problems, define MVP scope, make trade-offs, and drive to measurable customer outcomes.

Other signals

  • AI-native delivery
  • 0->1 product build
  • lead key areas of the product from concept through launch
  • make pragmatic architecture decisions
  • turn prototypes into a scalable product