As Senior Engineering Manager for Voice for Meetings, you will lead the engineering team building DeepL's real time AI voice products. This is a hands on leadership role. You will be accountable for what the team builds and how well it works, and you will be close enough to the code to have a real opinion about it
Lead, develop, and grow a small team of senior engineers, owning hiring, performance, and career development
Drive end to end delivery of real time voice features from architecture through to production, holding a high bar for quality and pace at the same time
Own the technical direction for your team's systems. Stay close enough to the detail to challenge senior engineers credibly and to get into the code yourself when it helps
Work in a tight loop with your Principal Product Manager and senior technical lead, shaping what gets built rather than receiving a finished plan
Operate confidently at the boundary with applied science. Understand how inference, preprocessing, and training data drift affect the product, and translate between model realities and delivery commitments
Make and communicate clear technical trade offs under real constraints, including latency budgets, model quality, and timelines
Spot risks and gaps beyond your own remit and fix them properly, leaving the teams around you better off rather than just unblocking yourself
Build a high agency, low bureaucracy culture where engineers are trusted to make decisions and take genuine ownership of outcomes
Qualities we look for
We care much more about how you think and what you have built than whether your background matches ours exactly
You have managed engineers directly and owned a team's delivery, performance, and development. You have hired people, grown them, and handled the difficult conversations as well as the good ones
You are a builder. You are comfortable in the trenches, you have real technical skills, and you have no interest in managing from a distance. If your hands on skills are a little rusty, that is fine, as long as you want to stay close to the work
You have led teams building product that users touch. Real time systems, streaming, distributed systems at scale, or similar. What matters is that technical depth was the substance of the work rather than a detail in the background
You have enough credibility to earn the confidence of senior engineers. You can reason about architecture, spot the failure modes, and add judgment to a technical decision rather than only facilitating it
You have a pragmatic working understanding of how ML systems behave in production. Inference, preprocessing, data drift, and what breaks when a model meets real users. You do not need to be an ML specialist
You think independently and act without waiting to be asked, and you draw those same instincts out of the people around you
You have strong product and commercial instinct. You frame engineering trade offs in terms of user and business outcomes
You are comfortable when the direction is still forming and priorities compete