# ML Systems Performance Engineer (MFU) — Higgsfield

> Source: <https://frontierroles.com/jobs/higgsfieldai-ml-systems-performance-engineer-mfu-0cc0bf/>
> Published: 2026-08-24 06:22:01+00:00

# ML Systems Performance Engineer (MFU)

- Salary
- Not published
- Location
- Almaty, Kazakhstan
- Work type
- On-site
- Posted
- today

[Apply on company site (opens in new tab)](https://jobs.ashbyhq.com/higgsfieldai/82db7018-fa8a-46fd-a5e1-71c5d9f67fcc/application)

Why work at Higgsfield AI?

Higgsfield AI is the fastest-scaling generative AI company in history, hitting $500M in annual revenue run rate, 25M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands. We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like.

What you will do

- Profile end-to-end training runs and identify bottlenecks across compute, memory, communication, storage, and orchestration.
- Define, measure, and improve MFU, tokens/sec/GPU, scaling efficiency, training goodput, and GPU uptime.
- Optimize distributed training and model-sharding strategies, including data, tensor, pipeline, context, and expert parallelism.
- Improve collective communication through topology-aware placement and compute/communication overlap.
- Develop or integrate optimized CUDA and Triton kernels
- Optimize data loading, preprocessing, sequence packing, and checkpointing so that I/O does not leave accelerators idle.
- Diagnose distributed hangs фтв performance regressions.
- Improve fault tolerance for long-running training jobs.

What we are looking for

- Strong experience running and optimizing multi-GPU or multi-node training.
- Experience with PyTorch Distributed or an equivalent training framework.
- Understanding of GPU architecture, including memory hierarchy, Tensor Cores
- Understanding of collective communication, cluster topology, and distributed-training bottlenecks.
- Experience with distributed parallelism technologies such as FSDP, DeepSpeed, Megatron-LM, TorchTitan, or similar.
- Ability to debug complex performance and reliability problems across multiple layers of the training stack.

Nice to have

- CUDA, Triton or GPU-kernel development experience.
- Experience with NCCL, MPI, UCX, RDMA, InfiniBand, RoCE, GPUDirect, NVLink, or NVSwitch.
- Experience training Mixture-of-Experts, multimodal, or reinforcement-learning models.
- Knowledge of PyTorch internals, torch.compile, XLA, ML compilers, or custom operators.
- Experience with mixed-precision training, including BF16, FP8, or FP4.

What We Offer

- Competitive base salary in USD, based on your experience, skills, and the scope of the role.
- Equity participation through the company’s stock option program, giving you the opportunity to share in Higgsfield’s long-term growth.
- Relocation support to Almaty for candidates moving from another city or country.
- A highly collaborative, fast-paced environment where you can work directly with experienced leaders and have a meaningful impact on the product and company.
- Opportunities for professional growth, ownership, and career development as the company scales.
- Company-provided equipment, meals, transportation, or other office benefits.

This is a fully on-site role based in our Almaty office. Our team works from the office five days per week for the full working day. We believe in-person collaboration is an important part of how we move quickly, solve complex problems, and build strong teams.