Applied Scientist

Responsibilities

• 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.
• Deliver Real-World Impact: Lead the design, implementation, and deployment of ML systems end-to-end, from research prototype to production.
• Shape the Roadmap: Contribute to research direction and product strategy, identifying which problems are worth solving and which methods are worth investing in.
• Hands-on Execution: Own implementation of data products at Adaption, addressing novel challenges in data, interaction, and evaluation with both creativity and engineering rigor.

Qualifications

• 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 (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.