About the role
Responsibilities:
- Design and implement continual learning algorithms for production systems.
- Develop sample-efficient training methods that learn from sparse interaction signals.
- Build retrieval and memory systems that adapt in real time.
- Run experiments, analyze results, and iterate quickly on research ideas.
- Optimize ML pipelines for latency and cost at scale.
- Contribute to technical direction and architecture decisions.
Requirements:
- Strong ML fundamentals: optimization, gradient methods, and neural architectures.
- 2+ years of hands-on training and deploying ML models.
- Python and PyTorch or JAX or an equivalent framework.
- Retrieval-augmented systems or reinforcement learning.
- The ability to read a paper, implement the idea, and run a rigorous experiment.
- Publications at top ML venues: NeurIPS, ICML, ICLR, ACL, or similar.