Large-Model Training Infrastructure Engineer (Relocate to SG)

Pivot Search; Singapore; On-site; SGD 180,000 - 300,000; Full time

Job summary

Pivot Search is seeking a dedicated ML Systems Engineer to build the training systems behind our Large Physics foundation Model. You will partner with researchers to move experiments from prototype to scalable production.

Responsibilities include designing and optimizing distributed training across thousands of GPUs, researching parallelisation and precision trade-offs, and building robust checkpointing and reproducibility infrastructure to support rapid research iteration.

Qualifications

• Must have demonstrated ownership of large-model distributed training.
• Deep understanding of parallelism, memory optimization and mixed precision.
• Ability to investigate performance from framework internals to kernels and collectives.

Responsibilities

• Design, implement and optimise distributed training across thousands of GPUs.
• Research parallelisation and numerical precision trade-offs for novel architectures.
• Profile and debug low-level GPU operations to improve throughput and utilisation.
• Build checkpointing, fault-tolerance and reproducibility systems for rapid research iteration.
• Collaborate with researchers to scale new model architectures.

Skills

Distributed training

Parallelism & memory

Performance profiling

ML frameworks familiarity

Job description

Pivot Search is seeking a dedicated ML Systems Engineer to build the training systems behind our Large Physics foundation Model. You will partner with researchers to move experiments from prototype to scalable production.

Responsibilities include designing and optimizing distributed training across thousands of GPUs, researching parallelisation and precision trade-offs, and building robust checkpointing and reproducibility infrastructure to support rapid research iteration.