Product Manager
Median ￥12,125,000 across 4 guides
Machine Learning Engineer Salary in Japan
Two of the five published 2026 Japan salary guides price this role: Hays and Robert Walters. Published figures run from ￥7,000,000 to ￥18,000,000, with a consensus median of ￥12,000,000.
Consensus Figures
1 USD = ¥154.04, European Central Bank rate for Sep 11, 2026
- ￥7,000,000≈ $45,440
- ￥12,000,000≈ $77,900
- ￥18,000,000≈ $116,900
- ￥11,000,000≈ $71,410
Where the Guides Land
The guides differ meaningfully on this role, with ￥11,000,000 between the lowest and highest published figure. Each guide surveys its own placements and draws its own role boundaries, which is why published ranges differ.
HaysRobert Walters
What Hiring This Role Costs via Agency
Fee and total acquisition cost on the consensus median of ￥12,000,000
Contingency Search (35%)
Multiple agencies working one role
- Agency fee
- ￥4,200,000≈ $27,270
- Total cost
- ￥16,200,000≈ $105,200
Typical range: 30-40%
Exclusive Search (25%)
A single trusted agency
- Agency fee
- ￥3,000,000≈ $19,480
- Total cost
- ￥15,000,000≈ $97,380
Typical range: 20-30%
Fee is industry-typical for the engagement model, computed on theoretical annual salary; not attributed to any single guide.
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Frequently Asked Questions
What is the median Machine Learning Engineer salary in Japan?
Across 2 published 2026 guides the consensus median comes to ￥12,000,000. The published figures range from ￥7,000,000 to ￥18,000,000.
How much does hiring a Machine Learning Engineer through an agency cost?
At the industry-typical contingency rate of 35% of theoretical annual salary, the fee on the median comes to ￥4,200,000. An exclusive engagement at 25% comes to ￥3,000,000. These rates are market norms and are never drawn from a named salary guide.
Which salary guides publish Machine Learning Engineer figures?
Hays and Robert Walters include this role in their 2026 Japan editions. The consensus shown here draws only on those published figures.
Why do the published figures differ by ￥11,000,000?
Each guide surveys its own placements and draws its own role boundaries, which is why published ranges differ. The spread shown is the gap between the lowest and highest published figure, never a modelled band.