DeepL27 days ago

Руководитель направления исследований | Промышленная инференция

Salary not specified

MARKET

65,900 ₽median for this role

AI Researcher · 37 jobs with disclosed pay

5,600half of the offers: 16,833–78,900143,300

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Полная занятостьLondon

Responsibilities

01Lead 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
02Own 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
03Act 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
04Drive 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)
05Operate 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
06Play an active role in identifying, assessing, and recruiting research and engineering talent as the team continues to develop

01PhD (preferable) in Computer Science, Mathematics, Physics, or a comparable quantitative discipline, or possess a strong ML/systems background with equivalent research depth
02Strong foundation in production ML systems, inference optimisation, or model serving at scale
03Proven 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
04Comfortable operating across the full model lifecycle from training handoff through to production deployment, monitoring, and efficiency improvement
05Understand infrastructure and compute constraints without needing to own them directly
06Excellent communication skills and the ability to translate complex technical direction into clear goals for both technical and non-technical stakeholders
07Solution-oriented and decisive, able to define direction and drive outcomes