Engineering Manager – Machine Learning

Role Overview

• As a hands-on team lead, you would operate along the following dimensions:
• Build and scale a high-performing team capable of tackling complex distributed ML challenges
• Own the full employee lifecycle: recruiting, onboarding, performance management, career development, and retention
• Empower your team members and help them grow in autonomy and technical expertise
• Mentor engineers at all levels, fostering a culture of continuous learning and psychological safety
• Create an inclusive environment where diverse perspectives drive innovation
• Define and execute technical roadmaps aligned with company objectives and product needs
• Lead resource allocation and capacity planning to balance team workload and business priorities
• Own FinOps responsibilities: optimize cloud costs, track spending, and ensure efficient resource utilization
• Ensure operational readiness through monitoring, incident response protocols, and system reliability practices
• Establish and track KPIs for team performance, system efficiency and health
• Design, develop, and maintain robust large-scale distributed training pipelines and ML infrastructure using cutting-edge technologies
• Lead architecture decisions for distributed systems that enable efficient model development at scale
• Hands-on contribution to critical technical challenges, including optimization of training pipelines and infrastructure
• Drive technical excellence through code reviews and architectural guidance
• Stay at the forefront of distributed training technologies and bring innovation to the team
• Partner closely with Product teams to translate business requirements into technical solutions
• Collaborate with (senior) Research Scientists to enable scalable model development and experimentation
• Work with Platform Engineering to ensure robust infrastructure and tooling
• Build strong relationships across engineering teams to drive alignment and knowledge sharing
• Communicate technical concepts effectively to both technical and non-technical stakeholders

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field.
• 6+ years of software engineering or ML engineering experience, with at least 2 years in a technical leadership or team lead role
• Proven track record of building and leading high-performing engineering teams.
• Experience guiding projects across the whole Software Development Life Cycle, from requirements through design to implementation, deployment and maintenance.
• Deep understanding of fundamental Machine Learning concepts and principles, familiarity with advanced model optimization techniques (such as distillation, graph optimization, quantization etc.)
• Significant experience with large-scale distributed training systems and frameworks (especially PyTorch and NCCL).
• Familiarity with GPUs, distributed systems, parallel computing and scaling laws.
• Advanced programming skills in Python, experience in performance-critical languages (C/C++ or CUDA) being a plus
• Familiarity of MLOps/DevOps best practices including CI/CD, Docker, Kubernetes, and observability, cloud platforms (GCP, AWS or Azure) and infrastructure-as-code
• Experience with Linux, version control, and container technologies
• Demonstrated ability in resource allocation, capacity planning, and FinOps principles
• Excellent problem-solving and data-driven decision-making skills in ambiguous situations

Tech Stack

• AWS
• Azure
• Cloud
• Distributed Systems
• Docker
• Google Cloud Platform
• Kubernetes
• Linux
• Python
• PyTorch
• SDLC

Benefits

• Cutting-edge AI research and development, with involvement of Charité, TU Berlin and our other partners
• Opportunity to shape the technical direction and grow into broader leadership roles
• Expand your skills by benefitting from our Learning & Development yearly budget of 1,000€ (plus 2 L&D days), language classes, and internal development programs
• Access to leadership development programs and executive coaching
• Flexible working hours and teleworking policy
• Enjoy your well-deserved time off within our 30 paid vacation days per year
• Family & pet friendly and support flexible parental leave options
• Pick a subsidized membership of your choice among public transport, sports, and well-being
• Enjoy our social gatherings, lunches, and off-site events for a fun and inclusive work environment
• Optional company pension scheme