Member of Technical Staff - ML Infra

Kindredventures; San Francisco (CA); On-site; USD 160,000 - 220,000; Full time

Job summary

Kindredventures is seeking an experienced ML infra/engineering professional to design, deploy, and maintain large distributed ML training and inference clusters in a production environment.

You will build scalable pipelines for petabyte-scale data and model training, explore parallelization and numeric precision trade-offs across varying model scales, and optimize GPU performance through profiling and low-level debugging.

Qualifications

• Experience with large distributed ML training and inference systems.
• Experience with cloud ML services on major clouds (GCP/AWS/Azure).
• Experience with containers and orchestration (Kubernetes/Docker).
• Experience with distributed task management and scalable model serving/deployment.
• Knowledge of monitoring, logging, observability, and version control for ML systems.

Responsibilities

• Design, deploy, and maintain large distributed ML training and inference clusters
• Develop scalable end-to-end pipelines to manage petabyte-scale datasets and model training throughout the ML lifecycle
• Research and test training approaches including parallelization and precision trade-offs across model scales
• Analyze, profile and debug low-level GPU operations to optimize performance
• Stay up-to-date on research to bring new ideas to work

Tools

Distributed training frameworks

Cloud platforms

Kubernetes

Docker

Distributed task management

Observability & version control

Job description

Responsibilities

• Design, deploy, and maintain large distributed ML training and inference clusters
• Develop efficient, scalable end-to-end pipelines to manage petabyte-scale datasets and model training throughout the entire ML lifecycle
• Research and test various training approaches including parallelization techniques and numerical precision trade-offs across different model scales
• Analyze, profile and debug low-level GPU operations to optimize performance
• Stay up-to-date on research to bring new ideas to work

What we’re looking for

We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.

• Strong grasp of state-of-the‑art techniques for optimizing training and inference workloads
• Demonstrated proficiency with distributed training frameworks (e.g. FSDP, DeepSpeed) to train large foundation models
• Knowledge of cloud platforms (GCP, AWS, or Azure) and their ML/AI service offerings
• Familiarity with containerization and orchestration frameworks (e.g., Kubernetes, Docker)
• Background working on distributed task management systems and scalable model serving & deployment architectures
• Understanding of monitoring, logging, observability, and version control best practices for ML systems

You don’t have to meet every single requirement above.