Research Manager | Production Inference

DeepL DeepL · AI Frontier · London, United Kingdom · Research

Research Manager for the Production Inference team, responsible for leading a team of research scientists and ML engineers focused on serving DeepL's language AI models reliably and efficiently at scale. The role involves people leadership, technical direction, roadmap ownership, and driving the performance of the model serving stack.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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).
  5. 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.

Skills

Required

  • PhD (preferable) in Computer Science, Mathematics, Physics, or a comparable quantitative discipline, or possess a strong ML/systems background with equivalent research depth.
  • 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.
  • Excellent communication skills and the ability to translate complex technical direction into clear goals for both technical and non-technical stakeholders.
  • Solution-oriented and decisive, able to define direction and drive for results.

Nice to have

  • 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.
  • 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.

What the JD emphasized

  • production inference
  • model serving
  • inference efficiency
  • reliability
  • cost performance
  • serving infrastructure evolution

Other signals

  • production inference
  • model serving
  • inference efficiency
  • latency
  • reliability
  • cost profile