Overview
We’re looking for a versatile Machine Learning Engineer to help build and optimize the core infrastructure that supports our AI research. This role involves working across the stack—from data processing and training efficiency to low-level GPU programming and performance tooling. You'll contribute to systems that are reliable, scalable, and tuned for high performance, while enabling researchers to better understand and improve their models. Ideal candidates are comfortable navigating complex systems, enjoy working across domains, and bring a strong engineering mindset paired with an interest in supporting innovative machine learning efforts.
What You’ll Do
- Design and implement high-performance data pipelines for processing large-scale datasets with an emphasis on reliability and reproducibility
- Apply the latest techniques to our internal training runs to achieve impressive hardware efficiency for our training runs
- Implement custom GPU kernels
- Create observability and benchmarking tools to help researchers understand the performance of their models/training runs
- Build and maintain secure sandboxed execution environments
What we’re looking for
- Experience with data pipeline development and ETL processes
- Possess strong systems programming skills and understand performance optimization
- Strong software engineering skills in Python
- Experience writing and optimizing custom GPU kernels
- Have contributed to observability, benchmarking, or performance-focused infrastructure at scale
- Familiarity of AI/ML workloads
- Comfortable working in ambiguous, fast-evolving environments and collaborating across disciplines
