Senior Machine Learning Engineering Manager

Overview

In this role, you lead the Production Inference team within DeepL’s Research organisation, guiding research scientists and ML engineers to deliver scalable, reliable model serving. You shape the roadmap for production inference systems, balancing near-term reliability with long-term research on efficiency and architecture. You act as the primary technical interface across research, infrastructure, and product teams to ensure impactful, well-scoped outputs. Your work drives latency, reliability, and cost improvements for DeepL’s language AI models at scale. This position offers a chance to influence production-grade AI systems in a fast-moving, purpose-driven environment.

Pay / Benefits

• Hybrid work, flexible hours
• Virtual Shares
• 30 days of annual leave
• Regular in-person team events
• Monthly full-day hacking sessions
• Competitive benefits

Responsibilities

• Lead and develop a high-performing team of researchers and ML engineers, fostering growth and rigorous technical delivery.
• Own the team’s research and development roadmap for production inference systems in collaboration with senior ICs and stakeholders.
• Serve as the main technical interface between the Production Inference team and adjacent functions to align research output with product needs.
• Drive reliability, efficiency, and cost performance of the model serving stack, including infrastructure decisions around load balancing and autoscaling.
• Operate with autonomy, defining direction in evolving product/market contexts.
• Contribute to talent identification, assessment, and recruitment for the team.

Key requirements

• PhD in Computer Science, Mathematics, Physics, or a strong ML/systems background (preferred) or equivalent deep research experience.
• Proven leadership of a team of researchers or ML engineers with track record of delivering high-quality, production-ready work.
• Experience across the full model lifecycle—from training handoff to deployment, monitoring, and improvement.
• Strong communication skills to translate complex technical direction for technical and non-technical stakeholders.
• Ability to operate autonomously and drive outcomes in ambiguous or evolving environments.

• leadership
• cross-functional collaboration
• clear communication
• production ML systems
• inference optimisation
• model serving at scale

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