As a software engineer on the ML Infrastructure team, you will work on developing the platform for orchestrating post-training and model evaluation jobs. At Scale, we are constantly developing new data sources and running experiments to understand their impact on ML models. To support this effort, we are looking for engineers who are comfortable navigating cloud infrastructure challenges as well as research challenges in benchmarking and tuning LLMs.
The ideal candidate is someone who has strong fundamentals in machine learning, backend system design, and has prior ML Infrastructure experience. They should also be comfortable with infrastructure and large scale system design, as well as diagnosing both model performance and system failures.
You will:
- Develop re-usable platforms for running in-house and open-source LLM-benchmarks.
- Ensure correctness and performance of post-training and eval jobs on the platform.
- Improve APIs for managing ML workflows.
- Contribute to foundational infrastructure at the company for model inference and training.
- Participate in our team’s on call process to ensure the availability of our services.
- Own projects end-to-end, from requirements, scoping, design, to implementation, in a highly collaborative and cross-functional environment.
Ideally you'd have:
- 4+ years of experience developing ML platforms.
- Passion for working closely with researchers to drive business impact.
- Experience training and/or benchmarking LLMs.
- Experience with Python, Docker, Kubernetes, and Infrastructure as code (e.g. terraform).
Nice to haves:
- Experience building, deploying, and monitoring complex microservice architectures.
- Experience working with a cloud technology stack (eg. AWS or GCP).