Jobiba Salary Intelligence
Senior Software Engineer Machine Learning Infrastructure salary in Austin
Salary range, market position, recent movement, location comparisons and matching live jobs—calculated from comparable Jobiba listings rather than copied salary tables.
Typical market range
Building confidence
We need at least five comparable recent salary observations with explicit currency and pay period before publishing a market estimate.
Analyze my salary →Salary distribution
Where the market sits
Market movement
Not enough historical evidence yet. Trend is shown only when both 90-day windows have enough comparable observations, preventing noisy “growth” claims from tiny samples.
Location intelligence
Compare Senior Software Engineer Machine Learning Infrastructure pay by market
Currencies are never blended or silently converted
San Mateo
72 samples$220,020
$220,020–$269,270 typical
San Francisco
56 samples$213,750
$182,500–$229,500 typical
Toronto
32 samplesCA$161,500
CA$136,000–CA$174,500 typical
New York
25 samples$216,000
$215,000–$226,800 typical
Mountain View
14 samples$193,930
$160,360–$193,930 typical
New York City
10 samples$198,500
$141,250–$212,500 typical
Bellevue
8 samples$243,750
$243,750–$243,750 typical
San Francisco Bay Area
8 samples$220,500
$208,188–$245,625 typical
- USA
8 samples$186,065
$186,065–$186,065 typical
Live opportunity layer
Senior Software Engineer Machine Learning Infrastructure jobs in Austin
Senior Software Engineer, Java
Austin, Texas, United States
$210K – $280K/yr
Listed salary · employer/source provided
Senior Software Engineer, Identity & Access Management (FedRamp)
Austin
$101K – $273K/yr
Listed salary · employer/source provided
Senior Software Engineer, Query Optimization
Austin
$101K – $273K/yr
Listed salary · employer/source provided
Senior Software Engineer I, Payments
Austin, TX
Senior Software Engineer, People Ops and AI Tools
Austin, TX, United States
How Jobiba calculates this
Evidence first, estimate second
- • Uses recent active Jobiba listings that contain employer/source salary data.
- • Requires an explicit salary currency and pay period before a listing can enter an aggregate.
- • Applies broad currency-specific plausibility floors/ceilings, then rejects extreme statistical outliers.
- • Groups title variants through Jobiba's canonical salary-role taxonomy so current estimates and historical trends use the same comparable cohort.
- • Annualizes hourly, daily, weekly and monthly pay; no foreign-exchange conversion is used.
- • Uses percentile medians and publishes a range only after the minimum comparable sample is reached.
- • Labels inferred pay on salary pages/jobs as a Jobiba estimate, never as employer-provided compensation.
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