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Salary
$22k – $54k per year (Estimated)
Location
In office (Almaty)
Employment
Full-Time
Overview
Company
Impact
Profile match
The True Engineer is an engineering leadership and software development publication platform hosted on Substack. Authored by Adlet Balzhanov, a senior software engineering lead with background in Big Tech and high-growth enterprise scale-ups, the publication delivers monthly long-form analytical essays, career frameworks, and strategic insights for software developers, engineering managers, and technology executives. Operating under a creator-driven digital subscription model, the newsletter focuses on technical leadership tactics, staff-level engineering practices, organizational dynamics within large tech companies, and navigating the operational impacts of AI on software development.

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.

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