ML Systems Engineer: Large-Scale Training & Pipelines

Mindbeam; United States; On-site; USD 150,000 - 230,000; Full time

Job summary

Mindbeam is building the next-generation AI infrastructure for open source and enterprise. We design scalable pre-training pipelines and optimize throughput across GPUs/TPUs and clusters.

You will collaborate with researchers to translate experimental setups into production-ready workflows, develop monitoring and fault-tolerance systems, and continuously benchmark and tune performance across stacks. A strong foundation in Python and ML frameworks is essential.

Qualifications

• 2+ years of experience with large-scale model training and distributed systems.
• Strong Python coding skills.
• Experience with ML frameworks (PyTorch, TensorFlow, JAX).
• Familiar with GPU scheduling, memory optimization, and parallelism strategies.
• Comfort with containerized environments (Docker/Kubernetes).

Responsibilities

• Build scalable pre-training pipelines for foundation models, optimizing throughput and efficiency.
• Implement distributed training strategies across GPUs/TPUs and high-performance clusters.
• Collaborate with researchers to translate experimental setups into production-ready workflows.
• Develop monitoring and fault-tolerance systems to ensure reliable large-scale training.
• Continuously benchmark and tune performance across hardware and software stacks.

Skills

Large-scale training

Distributed systems

Python

Performance optimization

Education

Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or related field

Tools

PyTorch

TensorFlow

JAX

Docker

Kubernetes

Job description

Mindbeam is building the next-generation AI infrastructure for open source and enterprise. We design scalable pre-training pipelines and optimize throughput across GPUs/TPUs and clusters.

You will collaborate with researchers to translate experimental setups into production-ready workflows, develop monitoring and fault-tolerance systems, and continuously benchmark and tune performance across stacks. A strong foundation in Python and ML frameworks is essential.