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.