Ai/ml Engineer
$110,000
avg. annual salary
Ai/ml Engineer
pays more on average
Site Reliability Engineer
$109,990
avg. annual salary
As technology continues to evolve, the demand for specialized roles in the tech industry has surged. Two in-demand positions are AI/ML Engineers and Site Reliability Engineers (SREs). Both play critical roles in developing and maintaining complex systems, but their focus and responsibilities differ significantly. This article compares the salaries, benefits, and career paths of these two roles, providing valuable insights for those considering a career in tech or looking to transition. Understanding these differences can help you make informed decisions regarding your career trajectory and earning potential in the rapidly changing job market.
Salary by Experience Level
starting salary
starting salary
avg. difference (0%)
Average Salary Overview
In 2025, the average salary for an AI/ML Engineer is estimated to be around $120,000, with a salary range from $100,000 to $150,000. In contrast, Site Reliability Engineers earn an average salary of approximately $115,000, with a range of $95,000 to $140,000.
While both positions offer lucrative compensation, the AI/ML Engineer typically commands a slightly higher salary.
Salary by Experience Level
Experience plays a vital role in determining earnings.
- •AI/ML Engineer: $80,000 - $100,000
- •Site Reliability Engineer: $75,000 - $95,000
Mid-level professionals in these fields tend to earn:
- •AI/ML Engineer: $110,000 - $130,000
- •Site Reliability Engineer: $100,000 - $120,000
Senior positions often offer substantial pay, with:
- •AI/ML Engineer: $140,000 - $180,000
- •Site Reliability Engineer: $130,000 - $160,000.
Benefits and Perks
Both AI/ML Engineers and Site Reliability Engineers enjoy a range of benefits beyond salary. Common benefits include health insurance, retirement plans, and bonuses.
AI/ML Engineers may also benefit from specialized training programs, access to cutting-edge technology, and the opportunity to contribute to innovative projects. On the other hand, Site Reliability Engineers often enjoy remote work options, performance bonuses, and a strong emphasis on work-life balance.
Career Paths and Growth Opportunities
Both career paths offer excellent growth opportunities. AI/ML Engineers can specialize further in areas like deep learning or natural language processing, potentially leading to roles such as AI Architect or Chief Data Scientist.
Similarly, Site Reliability Engineers can move into roles such as DevOps Engineer or Lead SRE, focusing on systems architecture and reliability optimization. Continuous learning and certifications can accelerate career growth in both fields.
Conclusion
In conclusion, while there are some differences in salary and job responsibilities, both AI/ML Engineers and Site Reliability Engineers offer rewarding career paths with excellent earning potential. Your choice between the two roles may depend on your interests, desired work environment, and long-term career goals.
Detailed Comparison: Pay, Skills, and Where to Earn More
Base salary ranges (U. S.
- •AI/ML Engineer: entry $90k–$110k, mid $120k–$160k, senior $170k–$250k.
- •Site Reliability Engineer (SRE): entry $85k–$105k, mid $110k–$145k, senior $150k–$220k.
Total compensation notes:
- •AI/ML roles often add 10–30% in bonuses and equity at startups; large tech firms push total comp higher by 20–40%.
- •SREs receive 5–20% in bonuses/equity; on-call and pager responsibility can justify higher base pay.
Skills that move the needle:
- •AI/ML: production MLOps (Docker, Kubernetes, TF Serving), model optimization (quantization to cut inference cost 30–60%), and demonstrable business impact (e.g., increased recommendation click-through by 8–15%).
- •SRE: distributed systems debugging, SLO/SLI design, observability stacks (Prometheus, Grafana), and cost optimization (reduce cloud spend 10–25%).
Industry and location effects:
- •Finance and SaaS typically pay 10–25% premium; Bay Area/NYC pay 15–40% above national averages; remote roles vary.
Actionable takeaways: quantify outcome (latency saved, cost reduced, revenue gained) and highlight production experience—those specifics drive salary offers more than titles.