United States
$150k–$240kMid–senior MLOps and ML-platform roles in major US markets; 2024–25 rounded band.
Work–life
60
Launches and platform incidents create peaks; hybrid work is common.
MLOps salaries are elevated because the role combines skills that companies often hire separately: cloud infrastructure, software delivery, data engineering and machine-learning operations. The title is not standardized, so comparable jobs may appear as ML platform engineer, ML engineer or AI infrastructure engineer.
Hiring follows AI investment cycles and is concentrated in technology, finance, health care and firms building internal AI platforms. Demand is strongest for engineers who can demonstrate production reliability and governance rather than only model experimentation.
The pay ladder
Junior ML platform role (US)EntryMLOps engineer (US)Early careerExperienced MLOps engineer (US)Mid careerStaff ML platform engineerSeniorPrincipal AI infrastructure leaderPeak
Junior ML platform role (US)
Rounded US early-career band in the mid-2020s; roles commonly require prior software or data experience.
MLOps engineer (US)
Typical mid-2020s US cash range across technology and data-intensive employers.
Experienced MLOps engineer (US)
Experienced platform and ML operations range in major US markets; equity varies.
Staff ML platform engineer
Senior US technology and finance compensation can include significant stock; not representative of all employers.
Principal AI infrastructure leader
Top US total compensation at leading technology firms in the mid-2020s; highly selective and variable.
Pay and life by country
Typical mid-to-senior packages, hours and leave — not entry stipends. Figures are rounded bands with a year and market in the notes.
South Korea
₩80M–₩140MExperienced AI-platform roles at larger employers; mid-2020s.
Work–life
48
Fast product cycles can extend hours, especially around launches.
Japan
¥8M–¥14MExperienced local and international employer roles; mid-2020s.
Work–life
54
Global teams can improve flexibility; deadlines still create overtime.
Germany
€75k–€115kMid–senior roles in major German markets; mid-2020s.
Work–life
73
Leave norms are strong, with smaller equity upside than top US firms.
United Kingdom
£70k–£115kMid–senior roles, especially London technology and finance; mid-2020s.
Work–life
65
Hybrid work is common; finance and startup roles may have sharper peaks.
Singapore
S$110k–S$190kMid–senior local and multinational AI-platform roles; mid-2020s.
Work–life
61
Regional coverage and high living costs affect the work-life trade-off.
What it pays around the world
United States$130k–$250k
SingaporeS$100k–S$190k
Germany€65k–€115k
United Kingdom£60k–£115k
Japan¥7M–¥14M
South Korea₩70M–₩140M
United States
Experienced MLOps and AI-platform compensation in major US markets, mid-2020s.
Singapore
Experienced roles at multinational and regional technology firms, mid-2020s.
Germany
Experienced ML platform roles in larger German markets, mid-2020s.
United Kingdom
London technology and finance roles can carry a substantial premium.
Japan
Experienced local and international employer bands, mid-2020s.
South Korea
Experienced AI-platform roles at larger technology and finance employers, mid-2020s.
Key numbers
Foundational paper
Common orchestration layer
Governance standard
Market demand
Who employs them
Builds cloud AI platforms, TensorFlow-related infrastructure and large-scale production ML systems.
Microsoft
Employs ML platform and MLOps engineers across Azure, products and AI services.
Amazon Web Services
Builds managed ML infrastructure and supports enterprise customers operating models in the cloud.
Databricks
Data and AI platform company employing engineers around ML lifecycle tooling and infrastructure.
NVIDIA
Builds accelerated-computing platforms and enterprise software for training and inference workloads.
JPMorgan Chase
Large financial-services employer using ML platforms under demanding governance and risk controls.
Where the demand is going
Organizations are moving from AI demonstrations to systems that must meet reliability, privacy and cost requirements. That shift supports MLOps demand, particularly where multiple teams need a common deployment and governance platform.
The title remains uneven: some employers hire an ML engineer to do MLOps, while others expect a platform engineer to learn ML. Candidates who can show both production engineering and sound evaluation practice have a clearer signal than candidates relying on tool names alone.
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