Staff Machine Learning Engineer - Deployment at Kodiak
San Francisco, CA
$200,000–$265,000
Jul 20, 2026
San Francisco, CA
Jul 22, 2026
What this job asks for AI summary
A staff-level role focused on taking machine learning models from training through to on-vehicle deployment for an autonomous trucking system. Day-to-day work centers on model optimization techniques such as quantization, pruning, and conversion to inference runtimes, GPU kernel work, and profiling to meet latency targets — alongside validating parity between onboard and offboard models. Suits experienced ML engineers with a strong production deployment background and comfort working across perception, planning, and infrastructure teams.
Staff level · 6+ years · San Jose-Sunnyvale-Santa Clara, CA · Full-time
Pay in the description: $200,000–$265,000
“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 75 people in the San Jose-Sunnyvale-Santa Clara, CA area plausibly meet what this posting asks for (data scientists). range 15–110
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $189,150 (middle half $152,859–$224,286). 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 (7)
The role is titled 'Staff Machine Learning Engineer' and is explicitly framed as a Staff-level position; the 6+ years of production ML/DL deployment experience and cross-team technical scope support that band.
SOC classification is Medium confidence: the role sits at the intersection of ML research/modeling (15-2051) and production software engineering (15-1252). The primary emphasis on deploying, optimizing, and profiling on-device models (quantization, TensorRT, custom GPU kernels) leans toward software development, but the ML fundamentals, model training, and data-driven improvement work also fit 15-2051. 15-1252 is listed as the runner-up.
Degree requirement is set to None: the JD states 'MS or PhD … or equivalent practical experience', making the degree substitutable.
The posted pay range ($200,000–$265,000) is described as spanning 'several internal levels' in the SF/Silicon Valley location; actual pay will vary by level and location.
The JD notes that some roles may require export-license review or U.S. person/citizenship verification due to DoD work, but no formal security clearance is listed as a hard gate.
HIL (Hardware-in-the-Loop) validation is mentioned as a technique but names no specific commercial tool, so it was not emitted as a named skill.
Ignored 1 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): GPU kernel development.
Read the full posting
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