Senior AI Infrastructure Engineer - Model Training
Kodiak · Mountain View, CA
$190,000–$260,000
Jul 20, 2026
Mountain View, CA
Jul 22, 2026
What this job asks for AI summary
This role focuses on building and optimizing the infrastructure that makes large-scale machine learning model training faster and more efficient. Day-to-day work involves designing high-throughput data pipelines for multimodal sensor data, tuning distributed training across multi-node GPU clusters, and squeezing maximum performance from modern accelerators. It suits engineers with hands-on experience in ML systems, distributed training frameworks, and GPU performance optimization.
Mid level · 2+ years · San Jose-Sunnyvale-Santa Clara, CA · Bachelor's required · Full-time
Pay in the description: $190,000–$260,000
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Roughly 140 people in the San Jose-Sunnyvale-Santa Clara, CA area plausibly meet what this posting asks for (software developers). range 30–200
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $217,796 (middle half $177,469–$231,052). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (10)
The title carries no seniority level; the stated experience requirement is only 2–3 years of industry experience, which maps to Mid-level despite the sophisticated technical scope.
The posted pay range ($190K–$260K) is described as spanning 'several internal levels' in the SF/Silicon Valley location, so the actual band for a given hire may be narrower.
Degree requirement: the JD lists 'BS, MS, or PhD' as a hard gate with no 'or equivalent experience' escape clause, so Bachelors is set as the minimum.
DeepSpeed and Megatron are listed together as interchangeable distributed training frameworks; DeepSpeed is used as the primary with Megatron in alternatives.
WebDataset and MosaicML Streaming/MDS are listed as interchangeable streaming dataset formats; WebDataset is primary with MosaicML Streaming in alternatives.
Nsight and PyTorch Profiler are listed together as profiling tools; Nsight is primary with PyTorch Profiler in alternatives.
NVLink and InfiniBand are listed together as interconnect technologies; NVLink is primary with InfiniBand in alternatives.
C++, CUDA, and Triton are explicitly called out as 'a plus' (preferred), not required.
The role sits at the intersection of ML infrastructure and systems engineering; 15-2051 (Data Scientists) is noted as a secondary possibility given the ML training focus, but the primary day-to-day work is building and optimizing software infrastructure.
Ignored 1 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): GPU performance optimization.
Read the full posting
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