Technical Product Manager

As a Technical Product Manager, you’ll be defining our product direction in close collaboration with our talented Engineering teams. You’ll own product areas end-to-end and ensuring we ship the right things at pace. You’ll bring technical credibility to every conversation and into product decisions that matter.​

You’ll work alongside the VP, Product in a product function that punches well above its size, complementing our engineering and ML teams with product leadership, aligning our roadmap across sales, research and GTM.​

​ This is a role for someone with genuine technical depth who wants to grow into more, using their hands-on background to drive product impact in one of the most technically interesting areas in AI today.

What will you be doing:

• Shipping Voice AI products end-to-end: From discovery and definition through to delivery and iteration. Focusing on shipping value to customers, not just features. That means owning the success definition for key product areas: choosing the right metrics and using data to drive iteration.​
• Speaking directly to customers: Running your own interviews with users building with our Speech APIs for use cases such as voice agents, medical scribes and more. Understanding their needs and communicating them to the business.​
• Evolving the product roadmap for your area: Bringing customer and market insight as well as technical context to build defendable prioritisation decisions.​
• Partnering closely with ML researchers and engineers: To translate technical requirements and realities into product decisions. Understanding what trade-offs need to be agreed and how to deliver efficiently.​
• Working with sales and GTM: Defining how we package and position what we build, so that what we ship stands out in a competitive market. ​
• Helping define what great product looks like at Speechmatics: Being part of a product culture built on rigour, speed and deep curiosity. ​

Who we’re looking for:

• Product experience: You've taken a product from problem definition to delivery multiple times and measured the impact.
• Technical depth: You’ve worked in software engineering, ML, data science or a related technical field. You're comfortable building scripts and quick prototypes, using the latest AI tools to move fast and validate ideas before committing engineering resource.​
• APIs and deployment: Familiarity with API-based products and SaaS or on-premise deployments would certainly help.
• ML Product fluency: You understand that shipping an ML model isn't like shipping a software feature. The definition of "ready to ship" is not black and white, evaluation isn’t pass or fail.​
• Experience working directly with teams that train, evaluate and ship models is a real advantage. If your background is outside ML, we'll want to understand how you build technical fluency quickly and any relevant experience.​
• Data instinct: You are comfortable digging into model evaluation results, asking whether the test data reflects real-world conditions, and understanding what a benchmark result does and doesn't tell you.​
• Engineering credibility: You can engage with ML engineers, researchers and developers from day one, earning trust by listening and learning, rather than