Role Overview
Build and scale distributed pre-training frameworks
· Set up DeepSpeed / FSDP / Megatron-LM across multi-node GPU clusters.
· Create robust launch scripts, resilient checkpoints, and job monitoring (e.g. NCCL/GLOO/GPU).
Turn mathematical ideas into fast production code
· Prototype new optimizers or attention methods (like in PyTorch/NumPy/JAX orothers).
· Convert them into efficient CUDA/Triton kernels with custom gradients and tests.
Boost training efficiency and stability
· Lead mixed-precision training, push bf16, fp8, etc, into daily runs, track their accuracy-vs-speed gains, and be able to analyze numeric stability
· Apply kernel fusion, communication tuning, and memory optimization to reach state-of-the-art throughput.
Accelerate research velocity
· Build logging, metrics, and other experiment-tracking tools for rapid iteration.
· Design ablation studies and statistical tests that validate—or refute—new ideas.
· Mentor interns and junior engineers through clear async design docs and code reviews.
You’ll work side-by-side with researchers, ship production code, and shape the future of large language models.
Why You’ll Love This Job
· Frontier-scale impact – Train and ship cutting-edge models powering MBZUAI research and industry collaborations.
· Research × Engineering blend – Move breakthrough papers into real systems and publish your own results.
· End-to-end mastery – Touch everything from petabyte data loaders to custom low-level kernels—experience that’s rare elsewhere.
· Open, mission-driven science – Join a transparent culture tackling problems that truly advance AI.
· Founding-team growth – Help set direction for IFM U.S. and lead the next generation of AI development.
Key Responsibilities
· Framework Ownership – Productionize a PyTorch/JAX pre-training stack and keep it reliable at scale.
· Custom Optimizer Implementation – Code new algorithms in distributed frameworks directly from mathematical specs.
· Experiment Infrastructure – Build reusable modules, logging, and metrics dashboards that speed up research cycles.
· Performance Optimization – Apply kernel fusion, communication optimization, and memory management to thousands of GPU jobs.
· Distributed Debugging – Rapidly diagnose gradient synchronization, collective-ops, or fault-tolerance issues.
· Collaboration – Document designs clearly, run post-mortems, and partner with global research teams.
Qualifications
Must-Haves
· 5 + years combined industry or hands-on research experience with large-scale deep-learning training.
· Led at least one large-scale transformer pre-training run
· Expert PyTorch or JAX/Flax plus DeepSpeed, FSDP, Megatron-LM, or MosaicML Composer.
· Experience with distributed training at scale (100+ GPUs).
· Proven multi-node GPU work (Slurm, K8s, or Ray) and NCCL/GLOO debugging.
· Strong software engineering skills on large ML codebases
· Ownership of mixed- or low-precision paths (bf16, fp8, 4-bit) with accuracy validation.
· Clear written communication (design docs, RFCs, post-mortems).
Nice-to-Haves
· NeurIPS / ICML / ICLR papers or open-source contributions to major ML frameworks.
· Experience implementing optimization algorithms (e.g., SGD variants, Adam, second-order methods).
· Background in numerical computing.
· Ability to translate math and
· build high-perf CUDA/Triton kernels.
Visa Sponsorship
This position is eligible for visa sponsorship.
Benefits Include
*Comprehensive medical, dental, and vision benefits
*Bonus
*401K Plan
*Generous paid time off, sick leave and holidays
*Paid Parental Leave
*Employee Assistance Program
*Life insurance and disability
Wireless Research Engineer
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