Applied Scientist
Applied Scientist drives research in efficient, adaptive ML including online learning and gradient-free methods, implements production ML systems, and shapes research/product roadmap. Requires 3-4 years ML experience deploying real-world systems.
About the job
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.
Skills
PyTorch, JAX, TensorFlow, Online Learning, Reinforcement Learning, Gradient-Free Methods, Data Modeling, Human Feedback, Reward Signals, Adaptive Learning
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