AI Talent at Maincode: What We’re Seeing

We've had over 400 applicants in the past 3 weeks for our AI Engineer role at Maincode, and we've noticed some super interesting trends...

The wave of AI talent coming through our doors at Maincode reflects more than just growth in the field—it signals a profound shift in how the next generation of engineers, researchers, and builders are approaching artificial intelligence.

We’re not just seeing more candidates. We’re seeing sharper specialisation, deeper expertise, and a much more research-driven mindset than ever before.

A New Breed of AI Engineers
Many of the applicants to our AI/ML team come with Master’s or PhDs, often in machine learning, deep learning, computer vision, or AI systems engineering. But it’s not just about degrees. What stands out is how focused these individuals are in their areas of interest—whether it’s fine-tuning transformer models for niche domains, building AI infrastructure at scale, or designing architectures that optimise performance for edge cases in production environments.

They’re not merely applying AI—they’re advancing it.

Python remains the lingua franca, but beyond that, we’re seeing a strong command of frameworks like PyTorch, TensorFlow, and JAX; familiarity with the latest research papers (and the instinct to question them); and an ability to think not just about models, but about systems—how models fit into real products, with real constraints and real users.

Builders With a Researcher’s Edge
What excites us most is the mindset. This new wave of AI professionals aren’t satisfied with off-the-shelf solutions. They’re builders who start with research but push toward application—quickly testing, iterating, and refining ideas to solve problems that matter. They treat every architecture, every pipeline, every evaluation method as something worth questioning, improving, or rethinking.

We’ve spoken with candidates who:

• Designed custom retrieval pipelines for domain-specific RAG systems
• Built privacy-preserving LLM applications for healthcare and legal sectors
• Optimised inference workflows for faster and cheaper large-scale deployment
• Conducted in-house benchmark testing for multi-modal models

This is the level of talent redefining what’s possible with AI.

As AI continues to scale into more areas of work and life, the margin for error grows smaller. Efficiency, reliability, safety, and relevance aren’t optional—they’re foundational. And that means companies need to bring in people who not only understand how to wield the tools of AI, but who also know how to shape them.

At Maincode, we’re building the next generation of AI-powered tools—starting with the browser. That means we need talent that goes beyond the surface level and gets deep into the architecture of intelligence. We’re looking for those rare individuals who think like researchers, build like engineers, and care deeply about real-world outcomes.

Come Build With Us
If you’re an AI/ML professional who thrives at the intersection of cutting-edge research and practical application, we’d love to hear from you.