The first ‘Who is Hiring’ thread of 2026 reveals a tech landscape shifting rapidly away from generic SaaS toward deep-tech frontiers like agentic AI, specialized robotics, and the ‘OS for biology.’
The Deep-Tech Renaissance
The current hiring market is dominated by companies moving beyond simple web applications and into complex, high-stakes infrastructure and physical automation.
- Agentic AI has moved from hype to infrastructure, with companies building dedicated data planes for autonomous agents.
- Bioinformatics is being redefined as the ‘OS for Biology’, integrating genomic data processing with drug discovery.
- Physical automation is tackling the ‘long tail’ of logistics and renewable energy construction.
At DeepL, we’re on a mission to build intelligent, trusted AI that helps organizations achieve their most important goals – in language and beyond – unlocking human potential and making work simpler, smarter and more connected.
Growth in these sectors is explosive, with some AI-driven biotech firms reporting they have 10x’d revenue within the past year alone.
The Complexity Barrier
As software moves closer to biology and heavy industry, developers are facing technical challenges that go far beyond standard web development patterns.
- Managing high-performance computing (HPC) nuances alongside biological data complexity.
- Scaling GPU inference for massive research models in enterprise environments.
- Fusing multi-modal data like RGB-D and LiDAR into unified 3D models for chaotic real-world environments.
We work fully on-site in Bilbao because the complexity of our problems (high-performance computing + biological nuances) requires high-bandwidth, in-person collaboration.
The shift toward onsite or hybrid work for core engineering teams highlights the difficulty of solving these multi-disciplinary problems in isolation.
The Path to Scalable Innovation
To overcome these hurdles, the industry is gravitating toward new engineering primitives and specialized architectural frameworks.
- Adopting durable execution platforms to manage state and reliability in complex distributed systems.
- Building agentic data planes that allow AI to reason and act on enterprise data in real-time.
- Utilizing modern stacks like Rust, Go, and Python to handle memory management and concurrency at scale.
Temporal enables developers to focus on writing important business logic, and not on managing state or worrying about the underlying infrastructure.
By abstracting away infrastructure concerns, teams are finally able to focus on the high-level logic required to build robots that assemble solar farms or AI that predicts antibody structures.
