Qualcomm Israel Ltd.
Engineering Group, Engineering Group > Modem Technologies
General Summary:
We are looking for a Senior/Staff AI Algorithms Engineer to join our research and engineering team, focusing on the development, training, and optimization of large-scale language models (LLMs) on distributed networks. The ideal candidate combines deep theoretical understanding with hands-on engineering excellence.
M.Sc. or Ph.D. in Computer Science, Electrical Engineering, Applied Mathematics, or a related field
5+ years of industry or research experience in machine learning / deep learning
Demonstrated track record of delivering production-quality ML systems or publishing in top-tier venues (NeurIPS, ICML, ICLR, TMLR, etc.)
Deep understanding of transformer architectures (encoder-only, decoder-only, encoder-decoder), attention mechanisms, positional encodings (RoPE, ALiBi, etc.), and normalization strategies
Hands-on experience training large-scale models (1B–70B+ parameters) from scratch
Familiarity with pre-training, instruction tuning, RLHF, DPO, and related alignment techniques
Knowledge of model evaluation: perplexity, downstream benchmarks (MMLU, HellaSwag, etc.), and ablation methodology
Strong practical experience with distributed training paradigms:
Data Parallelism (DDP, FSDP)
Tensor Parallelism (Megatron-style)
Pipeline Parallelism
ZeRO (Stage 1/2/3 with optimizer/gradient/parameter sharding)
Proficiency with modern training frameworks: PyTorch, DeepSpeed, Megatron-LM, Hugging Face Accelerate / Transformers
Experience managing large-scale GPU clusters (A100/H100/B200 or equivalent), including job scheduling, multi-node communication (NCCL), and GPU utilization monitoring
Expert-level Python programming; clean, testable, modular code
Proficiency with data pipelines for LLM pre-training
Solid understanding of profiling and debugging training runs: loss spikes, gradient norms, throughput bottlenecks (MFU), dead nodes
First/co-author publications in LLM training, efficient transformers, or distributed ML at top venues
Experience with novel architecture exploration: SSMs (Mamba), MoE, hybrid architectures
Familiarity with continual learning or domain adaptation
Experience with federated learning or layer-wise / alternative training strategies
Minimum Qualifications:
• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 4+ years of Software Engineering, Hardware Engineering, Electrical Engineering, Systems Engineering, or related work experience.
OR
Master's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 3+ years of Software Engineering, Hardware Engineering, Electrical Engineering, Systems Engineering, or related work experience.
OR
PhD in Computer Engineering, Computer Science, Electrical Engineering, or related field and 2+ years of Software Engineering, Hardware Engineering, Electrical Engineering, Systems Engineering, or related work experience.
*References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies.
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