About the role
- ML Infrastructure Engineer at ChipStack responsible for building training pipelines for LLMs. Collaborating with chip designers and software engineers in a fast-moving startup environment.
Responsibilities
- Build the core infrastructure that enables training, fine-tuning, evaluation, and deployment of LLMs across cloud and on-premise environments
- Work alongside highly experienced chip designers, ML scientists, and other top-notch engineers
- Contribute to solving some of the hardest problems in chip design
Requirements
- 5+ years of experience in ML infrastructure or adjacent roles
- Deep expertise in Python and experience with training frameworks like PyTorch or TensorFlow
- Strong systems engineering skills and experience with distributed training, data pipelines, and performance optimization
- Experience deploying ML models to production (REST APIs, batch jobs, streaming pipelines)
- Proficiency with cloud platforms (e.g., GCP, AWS) and containerized systems (Docker, Kubernetes)
- Experience managing GPU/TPU workloads efficiently
- Good communication skills and the ability to work directly with engineers and customers
- Prior experience training or fine-tuning LLMs
- Experience setting up observability, monitoring, and evaluation pipelines for ML models
Benefits
- Challenge status quo
- Strong opinions, loosely held
- Ship fast, ship quality
- Proud of our craft
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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