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

The ML/AI Engineering Manager at Mimica is responsible for leading a cross-functional team of ML Engineers and Software Developers, ensuring that projects align with company goals while maintaining technical excellence. This includes guiding the development of ML models, facilitating team meetings, resolving blockers, and fostering career growth within the team.

To apply for the ML/AI Engineering Manager role at Mimica, candidates should have a strong background in applied AI/ML research, especially in deep learning and computer vision, along with management experience in a startup environment. Familiarity with MLOps practices and excellent communication skills are also essential.

The ML/AI Engineering Manager at Mimica focuses on team development through one-on-one coaching, mentorship, and regular performance reviews. This nurtures a positive team culture and helps team members grow their careers while improving team dynamics and effectiveness.

In the ML/AI Engineering Manager role at Mimica, you’ll oversee projects that focus on the development and deployment of innovative ML models, as well as optimizing existing tools and infrastructure. Your work will directly influence how Mimica helps companies streamline their workflows.

Mimica offers a competitive compensation package that includes stock options, remote work flexibility, laptop stipends, generous paid time off, enhanced parental leave, and an annual learning and development budget, among other benefits tailored to support employees' well-being and professional growth.

When discussing my experience managing a remote team, I emphasize my strategies for communication, setting clear expectations, and fostering team collaboration through tools and regular check-ins to ensure everyone is aligned and motivated.

I've encountered various challenges, such as ensuring data quality and addressing scalability issues. I tackled these by implementing strong MLOps practices and promoting a culture of experimentation and feedback to navigate through complexities effectively.

I regularly read industry publications, attend webinars and conferences, and participate in online communities. Sharing insights with my team also helps us stay on the cutting edge of AI advancements together.

I promote an open-door policy where team members feel comfortable sharing their challenges. We employ brainstorming sessions and collaborative problem-solving strategies to tackle these challenges together.

Certainly! I led an ML model development project that optimized process automation for a large enterprise. The result was a 30% increase in efficiency, which significantly reduced costs and improved worker satisfaction.

I use a combination of stakeholder feedback and data-driven insights to prioritize projects that align with business objectives. Regular reassessment helps us adapt to changing circumstances quickly.

I focus on personalized mentorship through one-on-one sessions, encouraging junior engineers to take ownership of specific tasks while providing support. This builds their confidence and helps them develop their skills effectively.

Key metrics I consider include accuracy, precision, recall, F1 score, and AUC-ROC curves, depending on the project's goals. Continuous monitoring of these metrics allows for timely adjustments to improve model performance.

I would first facilitate a meeting to allow team members to express their perspectives. With an emphasis on collaboration, we can collectively analyze the situation and work toward a solution that aligns with our objectives.

Feedback is vital! I encourage a continual feedback loop where team members can provide and receive constructive criticism, fostering a culture of continuous improvement and allowing us to achieve our best collective output.