Machine Learning Engineer - Pre Training

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

About Mindbeam

We are building the next-generation AI infrastructure for open source and enterprise. Our work is deeply research-oriented and passionate about developing ground-breaking innovations to take state-of-the-art AI applications to the next level.

What drives us is not only advancing technology, but empowering the people behind it. We are a community of researchers, engineers, and visionaries who believe that collaboration, curiosity, and openness fuel progress. If you’re motivated by impact and inspired to build tools that others can build upon, you’ll be in the right place.

Mission

Design and optimize large-scale pre-training systems that power Mindbeam’s generative AI models.

Role Expectations

• 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.

Background

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or related field—or equivalent experience.
• 2+ years of experience with large-scale model training and distributed systems.
• Strong coding skills in Python and familiarity with ML frameworks (PyTorch, TensorFlow, JAX).
• Experience with GPU scheduling, memory optimization, and parallelism strategies.
• Comfort with containerized and orchestrated environments (Docker/Kubernetes).
• Understanding of high-performance computing and networking bottlenecks.

About You

You thrive on scale and complexity. You enjoy solving system-level bottlenecks, pushing hardware and software to their limits, and working closely with researchers to accelerate cutting-edge AI development.