Engineering Manager - Machine Learning - Remote

Overview

Mimica's mission is to empower enterprises, teams, and individuals to reclaim their most precious resource — time and work more efficiently, with greater purpose and impact.

Our AI-powered task mining observes employee actions across the desktop and categorizes them into detailed process maps. Mimica’s process intelligence highlights inefficiencies, prioritizes improvements based on ROI, recommends the optimal technology for automation (RPA, intelligent document processing, GenAI), and provides a blueprint for building new automations and transforming work.

Your mission

As the first Engineering Manager of our Machine Learning Team, you will own the strategic direction of the team's projects, ensuring alignment with business and product goals while addressing technical constraints.

You'll own the development of our next generation of models, guiding the team through technical challenges and delivery. You'll play a role in building a culture of good engineering and research practices, striving to maintain efficiency and technical excellence as we scale up.

Part of your day-to-day

• To lead, nurture and scale a remote team of 8+ machine learning 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.

Requirements

• Strong background as an applied AI/ML researcher, particularly in deep learning.
• Proven track record in managing growing teams, including hiring, mentoring, and developing talent.
• 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.
• Effective communication skills to engage with senior leaders, product teams, and engineers in complex technical discussions.
• 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 – articulating complex ideas, concepts, and trade-offs clearly and getting buy-in for strategic technical decisions.

Bonus

• Background in high-impact startup/scale-ups, driving iterative development and rapid delivery.
• Active interest in automation and task-mining.
• Familiarity with data pipeline architecture.
• Experience with secure software design and data protection mechanisms.

What we offer

💰 Generous compensation + stock options — aligned with our internal framework, market data, and individual skills.

🏢 Distributed work: Work from anywhere — fully remote, in our hubs, or a mix.

💻 Laptop, remote setup stipend, and co-working budget.

📍 Flexible schedules and location.

☀️ Ample paid time off, in addition to local public holidays.

🍼 Enhanced parental leave.

🧘‍♀️ Health and retirement benefits.

📖 Annual L&D budget.

🌴 Annual workaways and regular virtual & in-person socials.

🌍 Opportunity to contribute to groundbreaking projects that shape the future of work.

Note: Some benefits may vary depending on location