Updated 27 March 2026
Entry Level ML Engineer Salary
FAANG pays $180K-$280K total comp for entry ML engineers. Non-FAANG pays $100K-$150K. A PhD adds $20-$40K. The AI talent war has pushed starting salaries higher than senior software engineer roles at many companies.
Pay by Company Tier
FAANG / Top Tech
Google, Meta, Apple, Amazon, Microsoft, Netflix
$180K-$280K
Base: $130K-$160K
Stock grants are 30-50% of total comp. 4-year vesting.
Tier 2 Tech
Stripe, Uber, Airbnb, Databricks, Scale AI, Anthropic
$160K-$240K
Base: $120K-$150K
Competitive with FAANG. Pre-IPO equity can be worth much more.
Mid-size / Growth
Instacart, Notion, Figma, mid-stage startups
$130K-$200K
Base: $110K-$140K
Lower cash, higher equity upside potential.
Enterprise / Non-tech
JPMorgan, Capital One, Walmart, healthcare cos
$100K-$150K
Base: $95K-$125K
Lower total comp but often better WLB. Signing bonuses $10-$30K.
Startups (Seed-A)
Pre-product companies, AI labs
$85K-$120K + equity
Base: $85K-$120K
Cash is lower. Equity is lottery ticket. Could be worth $0 or $1M+.
Research labs
DeepMind, FAIR, Google Brain, OpenAI
$200K-$350K
Base: $140K-$180K
PhD required for most positions. Highest entry-level pay in ML.
Degree Premium
| Degree | Premium | Typical TC | Notes |
|---|---|---|---|
| BS Computer Science | Baseline | $120K-$150K TC | Competitive for MLE roles at most companies. Strong projects/internships required. |
| MS Computer Science (ML focus) | +$10-$20K | $140K-$180K TC | Standard for entry ML roles. 1-2 years of focused ML coursework + thesis/capstone. |
| PhD (ML/AI) | +$20-$40K | $180K-$280K TC | Required for research roles. Opens FAANG research scientist positions. Highest entry pay. |
| MS in adjacent field + ML bootcamp | +$5-$10K over BS | $110K-$140K TC | Physics, statistics, math + ML specialisation. Credible but not as valued as CS MS. |
The PhD question.
A PhD takes 4-6 years and costs $0 (fully funded in CS) but you forgo $500K-$800K in industry earnings during that time. The PhD premium at entry level ($20-$40K/year) takes 10-15 years to recoup the opportunity cost. The PhD is worth it if you want to do research, publish papers, and work at the frontier of ML. For production ML engineering, an MS with strong projects is sufficient and gets you earning 4 years sooner.