The opportunity
DeepL is a global AI product and research company focused on building secure, intelligent solutions to complex business problems. Over 200,000 business customers and millions of individuals across 228 global markets today trust DeepL's Language AI platform for human-like…
What you'll do
Lead and develop a high-performing team of research scientists and ML: engineers, building strong development plans, fostering a candid and non-retaliatory feedback culture, and maintaining high standards of technical rigour and delivery.
Own the team's research and development roadmap for production inference: systems, in close collaboration with senior ICs and cross-functional stakeholders, balancing near-term reliability commitments with longer-horizon research bets on inference efficiency and architecture.
Act as the primary technical interface between the Production Inference team: and adjacent functions — including foundational models research, voice research, applied research, infrastructure, and product — ensuring research output is well-scoped, well-communicated, and delivered without creating downstream bottlenecks.
Drive the reliability, efficiency, and cost performance of DeepL's model: serving stack, including strategic decisions around serving infrastructure evolution (load balancing, autoscaling, runtime selection, and hardware utilisation).
Operate with a high degree of autonomy, defining the team's direction and: pushing for results in an environment where requirements from product or commercial stakeholders can be ambiguous or evolving.
Play an active role in identifying, assessing, and recruiting research and: engineering talent as the team continues to develop.
What they're looking for
- You have proven experience leading a team of researchers or ML engineers,: with a track record of developing talent, maintaining delivery rigour, and holding the balance between research quality and production reliability.
- You have a strong background Computer Science, Mathematics, Physics, or a: comparable quantitative discipline, or possess a strong ML/systems background with equivalent research depth.
- You have a strong foundation in production ML systems, inference: optimisation, or model serving at scale — direct experience with LLM inference, speculative decoding, quantisation, or serving infrastructure is a meaningful differentiator.
- You are comfortable operating across the full model lifecycle: from training handoff through to production deployment, monitoring, and efficiency improvement — and understand infrastructure and compute constraints without needing to own them directly.
