Applied Scientist

Most AI is frozen in place - it doesn't adapt to the world. We think that's backwards. Our mandate is to build efficient intelligence that evolves in real-time. Our vision is AI systems that are flexible, personalized, and accessible to everyone. We believe efficiency is what makes this possible - it's how we expand access and ensure innovation benefits the many, not the few. We believe in talent density: bringing together the best and most driven individuals to push the boundaries of continual adaptation. We're looking for builders and creative thinkers ready to shape the next era of intelligence.

We're looking for an Applied Scientist who thrives at the intersection of applied research and real-world products. You'll push the frontier on efficiency, gradient-free exploration, real-time learning, and interface design — translating advances in these areas into adaptive ML systems that ship. You'll contribute meaningfully to both the research roadmap and product strategy, and lead the design and implementation of the systems that come out of it.

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  Advance the Research Frontier: Drive original work on efficient, adaptive ML — including online learning, gradient-free methods, and novel architectures — and turn those advances into systems that run in production.

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  Deliver Real-World Impact: Lead the design, implementation, and deployment of ML systems end-to-end, from research prototype to production.

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  Shape the Roadmap: Contribute to research direction and product strategy, identifying which problems are worth solving and which methods are worth investing in.

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  Hands-on Execution: Own implementation of data products at Adaption, addressing novel challenges in data, interaction, and evaluation with both creativity and engineering rigor.

• 3–4 years of industry experience in machine learning or applied research, with a track record of deploying ML systems that solved real business problems.

• Strong software engineering skills and fluency with ML frameworks (e.g., PyTorch, JAX, TensorFlow).

• Hands-on experience with online learning, reinforcement learning, or efficient ML architectures.

• Solid understanding of data modeling for training and how curation decisions shape model performance.

• Excellent communication skills and the ability to align technical work with high-level goals.

• A mindset of ownership, curiosity, and a bias toward action.

• Bonus: experience training or fine-tuning models using human feedback, reward signals, or other adaptive learning techniques.

Above all, we're looking for great teammates who make work feel lighter and aren't afraid to go out on a limb with bold ideas. You don't need to be perfect, but you do need to be adaptable. We encourage you to apply, even if you don't check every box.