Senior AI Performance Modeling Architect - TX, Austin, United States
Originally posted 13 October 2026 by the employer.
This role defines GPU architecture specifications that enable massive model training at scale.
About the role
What you'll do
- Lead performance modeling and optimization for multi-trillion parameter LLM training/inference including Dense, Mixture of Experts (MoE) with multiple modalities (text, vision, speech).
- Model/optimize novel parallelization strategies across tensor, pipeline, context, expert and data parallel dimensions.
- Architect memory-efficient training systems utilizing techniques like structured pruning, quantization (MX formats), continuous batching/chunked prefill, speculative decoding.
- Incorporate and extend SOTA models such as GPT-4, Reasoning models (Deepseek-R1), and multi-modal architectures.
- Collaborate with internal and external stakeholders/ML researchers to disseminate results.
What you'll need
- Extensive and Senior experience optimizing large-scale ML systems and GPU architectures.
- Deep expertise in CUDA programming, GPU memory hierarchies, and hardware-specific optimizations.
- Proven track record architecting distributed training systems handling large scale systems.
- Expert knowledge of transformer architectures, attention mechanisms, and model parallelism techniques.
- Bachelors, MS/PhD in Computer Science/Engineering or equivalent industry experience.
Nice to have
- Experience with PyTorch, CUDA, TensorRT, OpenAI Triton.
- Experience with distributed systems like Ray, Megatron-LM.
- Experience with performance analysis tools: NSight Compute, nvprof, PyTorch Profiler.
- Experience with KV cache optimization, Flash Attention, Mixture of Experts.
- Experience with high-speed networking: InfiniBand, RDMA, NVLink.
Skills: AI infrastructure, GPU architecture specifications, LLM training/inference, parallelization strategies, distributed ML systems, CUDA programming
This role has been open 1 day — well below the 79-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
516
|
Median days open
79 d
|
Median salary
$230k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | AMD | All employers we track in this specialty (516 roles · 76 employers) |
|---|---|---|
| Open roles in this specialty | 42 | 516 |
| Open roles in the wider Software, Firmware & Systems family | 409 | 6164 · 146 employers |
| Median days open | 78 d | 79 d (−1 d vs this employer) |
| Median salary (USD postings) | — | $230k |
Skills observed across this category: AI infrastructure, GPU architecture specifications, LLM training/inference, parallelization strategies, distributed ML systems, CUDA programming
Who's hiring in this category
- Qualcomm · 104 open roles · median 105 d
- NVIDIA · 92 open roles · median 51 d
- AMD (this employer) · 42 open roles · median 78 d
- Micron Technology · 27 open roles · median 68 d
- Mobileye · 20 open roles · median 72 d
- Analog Devices · 18 open roles · median 32 d
How we counted: 516 open AI/ML Hardware Engineering (AI ML Hardware) roles from 76 employers tracked in the SemiconductorJobs index, counted 14 Oct 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there — it is counted in the wider Software, Firmware & Systems family row. Figures refresh nightly.