Jobiba Salary Intelligence
Lead Staff Machine Learning Engineer Platform Identity salary in Canada
Salary range, market position, recent movement, location comparisons and matching live jobs—calculated from comparable Jobiba listings rather than copied salary tables.
Salary distribution
Where the market sits
$202,500
25th percentile
$224,750
Market median
$255,000
75th percentile
$199,250
Lower market
$202,500
25th percentile
$255,000
75th percentile
$267,500
Upper market
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 Lead Staff Machine Learning Engineer Platform Identity pay by market
Currencies are never blended or silently converted
Career growth explorer
Higher-paying roles to explore
These are same-currency market opportunities, not guaranteed promotions. Your private Career Coach narrows suggestions using your target role family, skills and profile.
Live opportunity layer
Lead Staff Machine Learning Engineer Platform Identity jobs in Canada
Machine Learning Engineer, Search Quality
San Francisco, Canada
$140K – $265K/yr
Listed salary · employer/source provided
Senior Machine Learning Engineer
Toronto, Canada
$202.5K – $255K/yr
Jobiba market estimate · early confidence
Machine Learning Engineer (Staff & Principal)
Toronto, Canada
$202.5K – $255K/yr
Jobiba market estimate · early confidence
Director, ML Engineering & Infrastructure
Toronto, Canada
$202.5K – $255K/yr
Jobiba market estimate · early confidence
Staff/Senior Machine Learning Engineer, Match Team
New York, NY or Los Angeles, Canada
$180K – $295K/yr
Listed salary · employer/source provided
Staff AI/ML Engineer
San Francisco, Canada
$240K – $270K/yr
Listed salary · employer/source provided
Senior AI/ML Engineer
San Francisco, Canada
$240K – $270K/yr
Listed salary · employer/source provided
Machine Learning Engineer, Assistant Quality
San Francisco, Canada
$180K – $205K/yr
Listed salary · employer/source provided
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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