Compare AI, ML, and data science salaries by role, experience level, and location. Estimate total compensation including equity and bonus.
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Typical: 15-40% of base at top cos
Typical: 10-25% at big tech
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ML Engineer • San Francisco / Bay Area
Base + $40,000 equity + $30,000 bonus
| Base Salary | $200,000 |
|---|---|
| Equity / RSUs | $40,000 |
| Annual Bonus | $30,000 |
| Total Compensation | $270,000 |
| Monthly Gross | $16,667 |
| Effective Hourly | $96 |
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The AI talent market remains one of the most competitive in tech. Companies are paying premium salaries because demand far outpaces supply — there are roughly 3 open AI/ML positions for every qualified candidate.
Base salary is only part of the picture. At companies like Google, Meta, and OpenAI, total compensation includes significant equity grants (RSUs or stock options) and annual bonuses of 15-30% of base. A senior ML engineer with a $200K base might earn $350-500K in total compensation.
ML Engineer ($160K median base): The workhorse of AI teams. Builds and deploys models in production. Strong demand across all industries — fintech, healthcare, autonomous vehicles, and SaaS.
AI Research Scientist ($195K median base): Pushes the frontier. Publishes papers, designs new architectures. Highest base pay but often requires PhD. Google DeepMind, Meta FAIR, and Anthropic are top employers.
Data Scientist ($135K median base): Analyzes data, builds models, drives business decisions. Broadest role — ranges from SQL analyst to PhD-level researcher depending on company.
Prompt Engineer ($130K median base): Newest role. Designs, tests, and optimizes prompts for LLMs. Lower base than traditional ML roles but growing fast as companies adopt AI tooling.
San Francisco commands a 25% premium over national average. NYC adds 15%. But remote work has compressed these gaps — many companies now offer"remote-US" rates that are 90% of Bay Area pay.
International salaries vary dramatically. London pays ~85% of US rates. Berlin ~70%. Bangalore ~35%. However, purchasing power parity makes some"lower" salaries competitive locally.
1. Specialize in production ML, not just research. Companies pay most for engineers who can ship models, not just train them.
2. Learn the business domain. An ML engineer who understands fintech or healthcare commands 20-30% more than a generalist.
3. Negotiate total comp, not just base. Equity at pre-IPO AI startups can be worth millions if the company succeeds.
4. Consider contract/consulting. Senior AI consultants charge $200-400/hour — often exceeding full-time total comp.
AI Research Scientists at top labs (Google DeepMind, OpenAI, Anthropic) earn $250K-$500K+ in total compensation. Staff/Principal ML Engineers can earn similarly.
Not always. Many ML Engineer and MLOps roles prioritize practical experience over degrees. Research Scientist roles often require PhDs, but industry experience can substitute.
Prompt engineers earn $90K-$180K depending on experience and location. It's a newer role with rapidly evolving compensation bands.
Yes. AI salaries grew 15-25% year-over-year from 2023-2025. The talent shortage ensures continued upward pressure on compensation.
Python, PyTorch, and TensorFlow remain essential. Cloud platforms (AWS SageMaker, GCP Vertex AI) are increasingly required. LLM fine-tuning and RAG architecture skills command 20-30% salary premiums. Strong foundations in statistics and linear algebra matter more than any single framework.
MLOps engineers earn $130K-$250K depending on experience and location. This role bridges ML engineering and DevOps, managing model deployment, monitoring, and infrastructure. Demand grew 300% from 2022-2025 as companies move from AI experiments to production systems.
Big tech companies (Google, Meta, Amazon) pay 20-40% more in total compensation through stock grants and bonuses. Startups offer lower base salary but potentially valuable equity. A senior ML engineer earns $200K-$350K at big tech vs $150K-$250K base plus equity at well-funded startups.
Start with online courses in machine learning fundamentals and deep learning. Build a portfolio of projects on GitHub. Contribute to open-source ML projects. Target ML Engineer roles first since they value software engineering skills. The transition typically takes 6-12 months of focused learning.
Estimated Salary = Base Pay × Level Multiplier × Location Multiplier
Total Comp = Base + Equity + Bonus. Data sourced from Levels.fyi, Glassdoor, and LinkedIn Salary Insights (2024-2025).
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Calculations are for educational purposes only. Consult a qualified financial advisor for personalized advice.