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Senior Machine Learning Infrastructure Engineer

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Senior Machine Learning Infrastructure Engineer in Others

  • PhysicsX
  • Full time
  • Email
  • Singapore, Singapore

Responsibilities

  • Design and operate distributed training infrastructure for neural operator architectures on NVIDIA DGX B200 systems.
  • Optimize training pipelines for throughput, fault tolerance, checkpointing, gradient accumulation, multi-node synchronization, and cost efficiency.
  • Build experiment tracking and observability systems for training runs, hyperparameter sweeps, and model performance.
  • Optimize data loading and cloud-storage I/O for large-scale mesh datasets through prefetching, caching, and format optimization.
  • Build model-serving and packaging infrastructure for pre-trained Large Physics Models, including customer deployment and fine-tuning capabilities.
  • Ensure reproducible model checkpoints and reliable deployment behavior across customer environments.
  • Improve Research developer experience through debugging tools, fast iteration cycles, and collaboration on shared infrastructure standards.

Requirements

  • At least 5 years of experience building and operating ML infrastructure at scale.
  • Deep expertise in distributed training, including NCCL debugging, collective communication, FSDP, DDP, and pipeline parallelism.
  • Strong systems fundamentals across Linux, networking, NVLink, InfiniBand, storage I/O, profiling, and performance optimization.
  • Production experience with Kubernetes and SLURM for GPU-cluster job orchestration.
  • Proficiency in Python and ML frameworks, with PyTorch strongly preferred.
  • Experience with cloud GPU infrastructure, ideally CoreWeave or a similar GPU/HPC-focused cloud.
  • Strong project-scoping, problem-solving, analytical, collaboration, and communication skills, particularly in research settings.
  • Experience with geometric deep learning, neural operators, mesh/point-cloud/graph architectures, HPC simulation workflows, model serving, experiment tracking, observability, or customer deployment packaging is preferred.

Benefits

  • Hybrid work model combining Shoreditch office time with work-from-home days.
  • Equity options and a 10% employer pension contribution.
  • Free office lunches, enhanced parental leave, YellowNest nursery scheme, and 25 days of annual leave plus public holidays.
  • Private medical insurance, Wellhub subscription, eye tests, personal development support, and an Employee Assistance Programme.
  • Bike2Work scheme, season ticket loan, and Octopus EV salary sacrifice.

PhysicsX

PhysicsX builds an AI-driven simulation software stack that enables high-fidelity, multi-physics modeling and optimization for engineering and manufacturing teams. The company sells software and delivery services to enterprises in aerospace and defense, automotive, semiconductors, materials, and ...

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