Choose from a wide range of CV templates and customize the design with a single click.
Use ATS-optimised CV and resume templates that pass applicant tracking systems. Our CV builder helps recruiters read, scan, and shortlist your CV faster.
Use professional field-tested resume templates that follow the exact CV rules employers look for.
Create CV
Use professional field-tested resume templates that follow the exact CV rules employers look for.
Create CVIf you're searching for AI engineer salary, you're not just looking for averages—you’re trying to understand how to break into one of the highest-paying roles in tech and how compensation actually works at the top end.
This guide goes beyond surface-level salary data. It explains:
Real salary ranges (entry → elite levels)
How recruiters and hiring managers decide your compensation
What separates a $120K AI engineer from a $400K+ one
AI engineering is now one of the highest-paid roles in the entire tech industry.
Entry-Level (0–2 years): $80,000 – $120,000 :contentReference[oaicite:0]
Mid-Level (2–5 years): $120,000 – $170,000 :contentReference[oaicite:1]
Senior (5–8 years): $160,000 – $230,000 :contentReference[oaicite:2]
Staff / Lead AI Engineer: $200,000 – $300,000+ :contentReference[oaicite:3]
Elite (Big Tech / AI Labs): $300,000 – $600,000+ total comp :contentReference[oaicite:4]
Median salary: ~$140K–$150K
AI engineers don’t just build features—they build systems that scale revenue, automation, and decision-making.
Examples:
Recommendation engines → increase sales
LLM integrations → reduce operational costs
Predictive models → drive business strategy
There are many “AI learners”… but very few engineers who can:
Deploy models into production
Optimize performance at scale
Integrate AI into real products
$80K – $120K
Often labeled as: ML Engineer I, Junior AI Engineer
What gets you hired:
Strong Python + ML fundamentals
Real projects (not Kaggle-only profiles)
Understanding of model deployment basics
Expectations:
Most common range: $160K–$170K
Average total compensation (Big Tech): ~$240K+
Recruiter Insight:
AI engineer compensation is extremely top-heavy. The top 10% earn 2–5x more than the average.
Hiring Manager Reality:
We don’t pay for “AI knowledge.”
We pay for production-grade AI systems that work under real constraints.
Companies are reallocating budgets aggressively toward AI.
AI is replacing roles across industries
Companies are competing for top AI talent globally :contentReference[oaicite:8]
This drives salaries upward at the high end.
Build and deploy ML pipelines
Work with data engineering teams
Improve model accuracy and efficiency
What defines seniority:
Own end-to-end AI systems
Optimize models for scale
Influence architecture decisions
This is where compensation explodes.
You are evaluated on:
System design at scale
Cross-team impact
Strategic AI direction
$300K – $600K+
Can exceed $1M with equity
Examples:
Meta engineers up to $450K base :contentReference[oaicite:9]
OpenAI engineers up to $460K base (excluding stock) :contentReference[oaicite:10]
This is where most candidates misunderstand compensation.
Biggest salary differentiator:
Not:
But:
High-paid AI engineers understand:
MLOps
Distributed systems
Data pipelines
Cloud (AWS, GCP)
Highest-paying niches:
LLM / Generative AI
Reinforcement learning
Computer vision (real-world applications)
AI infrastructure
Top engineers connect AI to outcomes:
Revenue
Cost reduction
User growth
Media & Tech: ~$190K+
Information Technology: ~$167K+
Consulting: ~$156K+
Stripe: ~$414K
DoorDash: ~$275K+
AI Engineer: $140K – $300K+
Data Scientist: $100K – $180K
Software Engineer: $110K – $200K
ML Engineer: $130K – $250K
Insight:
AI engineers command higher salaries due to cross-disciplinary complexity + business impact.
Your resume determines your salary bracket before interviews even start.
Problem → Solution → Impact → Tech
Focus on production systems
Quantify results
Candidate Name: Alexander Chen
Job Title: Senior AI Engineer
Location: San Francisco, CA
PROFESSIONAL SUMMARY
Senior AI Engineer with 8+ years of experience designing and deploying large-scale machine learning systems and generative AI applications. Proven track record of driving business impact through scalable AI solutions.
CORE SKILLS
Python
TensorFlow / PyTorch
LLMs (GPT-based systems)
MLOps
AWS / GCP
Data Engineering
Distributed Systems
PROFESSIONAL EXPERIENCE
Senior AI Engineer – OpenAI – San Francisco, CA
2022 – Present
Led deployment of large-scale LLM system serving millions of users
Reduced inference latency by 38%, improving user engagement by 21%
Designed scalable AI infrastructure supporting multi-region deployment
AI Engineer – Google – Mountain View, CA
2019 – 2022
Built recommendation system increasing user retention by 17%
Optimized ML pipelines reducing training costs by 25%
Collaborated cross-functionally to integrate AI into core products
EDUCATION
Master’s Degree in Artificial Intelligence
Low salary:
High salary:
Revenue-generating systems
Automation tools
AI-powered products
Salary differences are massive:
Startup: $120K – $180K
Big Tech: $180K – $400K+
Internal raise: 5% – 10%
External jump: 20% – 50%
Top candidates:
Combine ML + engineering + business thinking
Communicate impact clearly
Show ownership
Many candidates:
Train models
Experiment
But never:
→ This caps salary significantly
Weak Example:
“Worked on machine learning models.”
Good Example:
“Deployed real-time recommendation engine improving revenue by 15%.”
Knowing tools ≠ high salary
Impact = high salary
AI engineers without MLOps knowledge are often underpaid.
LLM expertise is now one of the highest-paid skill sets globally.
Top companies are offering:
Massive salaries
Equity packages
Research freedom
Top 10% → $300K+
Average → $140K
Gap is widening rapidly.
They focus on high-impact systems (LLMs, infrastructure, AI platforms) and join high-growth startups or scale-ups where equity and impact drive compensation beyond base salary.
LLMs, MLOps, distributed systems, and real-world deployment experience increase salary faster than academic knowledge or certifications.
The difference is production impact. Engineers building scalable AI systems tied to revenue or cost savings are compensated far higher than those working on isolated models.
It helps for research-heavy roles, but in most industry positions, production experience outweighs academic credentials.
Extremely important. Moving from a low-paying company to a high-paying one can increase total compensation by 50–200% instantly.
Upload CV
Import from Linkedin
