Executive Summary
ML6 takes a skill-based approach to building diverse AI engineering teams, complementing traditional diversity metrics such as gender or nationality. By broadening our scope and also focusing on complementary skills, perspectives, and evolving technical needs, ML6 creates teams capable of delivering production-ready AI systems. This approach is supported by data-driven workforce planning, inclusive job design, and a strategic shift from machine learning roles to broader AI engineering capabilities.
At ML6 our aim is to build bold ideas and we are convinced you cannot do that with a uniform team. True innovation doesn't come from a team of similar personalities and backgrounds, no matter how brilliant they may be. Real strength lies in a team with different profiles and backgrounds, diverse angles and unique perspectives.
But what does ‘diversity’ truly mean for an AI engineering company in 2026?
How to Build Diverse Teams in AI and Tech
For us, it goes beyond quotas. It's about actively building a team that thinks differently and complements each other.
What Is Skill-Based Diversity in AI Hiring?
Skill-based diversity refers to building teams based on complementary skills, experiences, and ways of thinking, rather than relying solely on traditional credentials or job titles.
The World Economic Forum highlights the growing shift toward skills-based hiring, emphasizing that focusing on capabilities rather than degrees enables organizations to tap broader, more diverse talent pools.
This approach is increasingly used in modern HR systems and recruiting strategies, including resume screening tools and AI-powered matching, to better align candidate profiles with evolving job roles and skills requirements.
Skill-Based Diversity at ML6: A Data-Driven Approach
With a team of 22 nationalities, a mix of seniority levels and ages, and people from diverse educational backgrounds, we’re on the right track to building a diverse workforce. This starts with not staring ourselves blind on a single dimension of diversity, such as 'women in tech'. While gender balance remains a priority, our vision of diversity is far broader, including neurodiversity, cultural diversity, and, of course, skill-based diversity to ensure we bring truly unique perspectives to the table.
So, instead of defaulting to quotas, we use analytics to identify these diversity gaps and build a diverse workforce.
By working with data, we can define effective actions rather than chasing a fixed number and focus more on "skill-based diversity." While we certainly take factors like gender, nationality, and age into account as essential building blocks for building out a diverse team, our vision for diversity extends beyond these traditional labels. We start with the question: What skills, perspectives, and expertise do we need today that complement the team we already have? This is a dynamic process which requires a thorough workforce planning - something we are fully committed to developing.
This approach is supported by workforce planning and skill gap analysis, allowing us to continuously assess which capabilities are missing and where we need to evolve as a team.
Why Skill-Based Hiring Matters in AI Engineering
Skill-based hiring is becoming essential in AI engineering, where the ability to build, deploy, and scale systems depends on a combination of complementary skills rather than rigid roles. As AI projects move from experimentation to production, organizations need teams that blend machine learning expertise with software engineering, data infrastructure, and domain knowledge.
Research consistently shows that diversity is directly linked to both decision-making quality and business performance. Studies from Salesforce (via Cloverpop) indicate that diverse teams make better decisions up to 87% of the time, while McKinsey & Company finds that companies in the top quartile for diversity are up to 25–36% more likely to outperform financially. This highlights that combining different skill sets and perspectives is not just beneficial; it is a measurable driver of better outcomes.
By focusing on capabilities rather than credentials, companies can build more adaptable teams better equipped to deliver real-world AI solutions.
Diversity is no longer just an HR initiative; it is a capability-building strategy for AI organizations.
Rethinking Job Descriptions to Attract Diverse Talent
This search for complementary skills also dictates how we approach talent with flexibility. This means that our job postings are a starting point, not a rigid checklist, where we explicitly encourage candidates not to be demotivated if they can't tick every single box.
We recently reviewed our job posts, specifically our wording, to ensure we aren't unintentionally skewing toward a specific demographic and to add a diversity statement. By using more inclusive language and focusing on core competencies, we aim to lower the threshold for talented individuals to envision themselves at ML6. We want our vacancies to be more than just a list of requirements: they should also reflect our company culture. We want to provide insights into how we work and highlight our goal of building an inclusive culture of trust.
From Machine Learning to AI Engineering: A Shift in Skills
As the AI landscape evolves, so does the technical DNA of our team. We’ve noticed a significant trend: the challenges our customers face today require a much broader skillset than traditional Machine Learning or Data Science alone. Our projects move at the speed of the latest industry developments, with a growing focus on scaling AI solutions and ensuring they are production-ready.
To reflect this end-to-end responsibility, we have shifted our focus toward AI Engineering. This isn't just a change in title; it’s a strategic diversification of the skillsets we recruit. By emphasizing engineering excellence alongside machine learning expertise, we bring in the skills needed to build robust, integrated systems.
At ML6, we thrive in an environment of constant growth and change. This fast-paced nature is perhaps our greatest challenge, but also our biggest opportunity. It forces us to constantly reinvent ourselves. In such a dynamic field, staying relevant means being lifelong learners - not just from books or courses, but primarily from each other. By bringing together these diverse "AI Engineering" profiles, we create a feedback loop where different expertises challenge and elevate one another every single day.
The Future of Workforce Diversity in AI
Building a diverse team is the foundation, but how do we ensure that everyone is rewarded fairly and given equal opportunities to grow?
In our next blog post, we dive deeper into the world of Equity and Fairness. We’ll explain how our "Salary House" provides a transparent roadmap for every career path and how we proactively ensure that growth at ML6 is accessible to everyone, regardless of their background.
Explore Opportunities at ML6
At ML6, we’re continuously looking for people who bring unique perspectives, complementary skills, and a passion for building real-world AI systems.
If this approach to skill-based diversity resonates with you, explore our open roles and discover how you can help shape the future of AI engineering.
