The role

We’re looking for a Senior Software Engineer to help strengthen the Quality Engineering foundation across our SDK teams.

This role focuses on building confidence in the quality, reliability, and delivery of SDK solutions across multiple platforms. You’ll work on automation, CI/CD, tooling, test strategy, diagnostics, reporting, and engineering practices that help teams build quality into their work from the start.

You’ll work across several SDK teams to understand quality risks, improve workflows, and turn broad or ambiguous problems into practical engineering outcomes. This role needs someone with a strong testing mindset, good technical judgement, and the ability to improve both the systems we use and the way teams think about quality.

We’re looking for someone who can operate beyond traditional test automation. The right person will understand how to identify risk, ask good questions, design useful test approaches, improve developer feedback loops, and build tools or processes that make quality easier to achieve.

You don’t need to have worked with every tool or domain listed below. You should have strong engineering fundamentals, be comfortable learning unfamiliar technical areas, and be able to apply that learning to real quality engineering problems.

What you’ll be doing

• Building and improving CI/CD pipelines that support SDK build, test, validation, and release workflows.
• Developing Python-based automation and tooling for test orchestration, system validation, diagnostics, reporting, and quality visibility.
• Creating, maintaining, and improving automated tests across unit, integration, system, and end-to-end levels.
• Applying a testing mindset to engineering problems, including identifying risks, questioning assumptions, improving observability, and designing solutions that are easier to validate and maintain.
• Helping teams bake quality into solutions early by influencing design, implementation, testability, diagnostics, and release confidence.
• Investigating issues across build, test, deployment, and execution environments, then improving the workflows that allow teams to detect, diagnose, and prevent similar issues.
• Supporting Linux and Windows development environments, including cross-platform scripting, automation, debugging, and build/test workflows.
• Using test frameworks, quality metrics, reporting, and engineering data to improve quality visibility and support practical test strategy.
• Working with SDK teams to clarify quality risks, align on shared initiatives, and turn broad goals into actionable engineering work.
• Identifying bottlenecks, fragile processes, gaps in automation, weak feedback loops, or weaknesses in test coverage, then driving pragmatic improvements with the teams involved.
• Using AI-assisted and agentic engineering technologies responsibly to improve coding, debugging, test generation, refactoring, documentation, triage, and team efficiency.
• Building domain knowledge across SDK areas through planned rotations or focused assignments, contributing useful improvements in new technical contexts.
• Balancing immediate delivery needs with longer-term improvements to SDK quality engineering practices, CI/CD reliability, developer feedback, and test effectiveness.
• Coordinating with teams across London, Bristol, and Dublin, including occasional planned travel where useful.
• Mentoring peers, sharing good engineering practices, and helping influence quality engineering approaches across teams.

What we’re looking for

• Strong practical Python skills for engineering automation, tooling, diagnostics, test orchestration, or quality-focused workflows.
• Experience building, maintaining, or improving CI/CD, build, test, release, or deployment automation, using tools such as Jenkins/Groovy or equivalent systems.
• Experience developing, maintaining, or improving automated tests, test frameworks, test infrastructure, or quality-focused tooling.
• A strong testing mindset, including the ability to identify risk, understand failure modes, question assumptions, and design practical approaches to build confidence in complex systems.
• Experience applying quality engineering practices beyond test execution, such as improving testability, observability, diagnostics, developer feedback, reporting, and release confidence.
• Ability to diagnose and resolve issues across complex build, test, CI, deployment, or execution environments.
• Comfortable working across Linux and Windows development environments.
• Familiarity with test frameworks such as pytest, GTest, or similar, and an understanding of quality metrics, test reporting, or practical test strategy.
• Experience using AI-assisted coding tools, LLMs, or agentic technologies to improve engineering productivity, with an understanding of how to review, validate, and apply generated output safely.
• Ability to work with ambiguity: asking good questions, identifying risks, breaking broad problems into practical steps, and making sound engineering decisions when every detail isn’t defined upfront.
• Pragmatic engineering judgement, including knowing when to build a robust long-term solution and when to deliver a focused improvement that unblocks progress.
• Clear communication and collaborative problem-solving, especially when working across distributed teams, resolving unclear ownership, and advocating for quality engineering concerns.
• A willingness to take responsibility for quality engineering improvements, including proposing practical options, implementing changes, and following up with the teams involved.

Useful additional experience

Experience or awareness in 1 or more of the following areas would be useful, but we don’t expect expertise in all of them:

• Languages such as C, C++, C#, or Rust.
• Build systems such as CMake or similar.
• .NET development or testing workflows.
• Container, Kubernetes, or VM-based environments.
• Desktop application testing.
• Graphics APIs or graphics-adjacent testing.
• AI/ML, MLOps, or data-driven quality workflows.
• Large-scale test infrastructure, reporting, or quality analytics.
• Developer productivity, platform engineering, or internal tooling.
• Experience introducing or improving AI-assisted engineering workflows within a team.

About your background

We know strong engineers come from many different career paths. You don’t need to match every item in this advert to be considered.

The most important experience for this role is practical engineering work that improves quality, confidence, reliability, or delivery. That could come from quality engineering, SDET, test infrastructure, automation, CI/CD, developer tooling, platform engineering, or complex technical debugging.

We’re looking for someone who can operate at a senior level, work well with others, and help raise the quality engineering bar across SDK teams.

You should be comfortable thinking beyond individual tests or pipelines. We want someone who can help teams understand risk, build better feedback loops, improve testability, make delivery workflows more reliable, and use technology, including agentic AI, to improve how engineering teams work.

If that sounds like work you’ve done, or a natural next step from your experience, we encourage you to apply.