ML/AI Engineering Manager, Computer Vision - UK/Europe

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Requirements:

• Strong background as an applied AI/ML researcher, particularly in deep learning (ideally advanced NLP and/or Computer vision)
• Proven track record in managing growing teams, including hiring, mentoring, and developing talent.
• Background in high-impact Startups/Scale-ups, driving iterative development and rapid delivery.
• Experience leading machine learning/data science technical initiatives, particularly in high-growth and large-scale production environments.
• Deep understanding of good ML engineering practices, including MLOps, data engineering, and scalability.
• Strong analytical and troubleshooting skills – methodically decomposing systems to identify bottlenecks, determine root causes and implement effective solutions.
• Drive to continually develop your skills, improve team processes and reduce debt.
• Fluency in English, with effective communication skills: being able to articulate complex ideas and trade-offs clearly to a diversity of stakeholders.

Responsibilities:

• Lead, nurture and scale a remote team of 5-8 members, including ML Engineers and Software Engineers, supporting their career development through 1:1s, coaching, mentorship, and performance reviews.
• Leading project management discussions, coordinating and facilitating the Weekly planning and team meetings.
• Collaborate with the CTO, Platform and Product to align team priorities with company OKRs.
• Collaborate with the People team on recruiting and onboarding talent that matches our values and technical excellence by being a part of interviewing, debriefs, defining scorecards and onboarding plans.
• Facilitate and lead discussions that drive the development and deployment of our new generation of ML models, optimizing tools and infrastructure for efficiency, while upholding engineering excellence.
• Identifying and resolving bottleneck and efficiency blockers, enabling the team to iterate faster.
• Championing initiatives to improve the quality, security and performance of our systems, processes and code.
• Promoting a culture of collaboration, transparency, feedback and continuous learning.