ML Engineer Salary San Francisco 2026: What the Bay Area Pays

# ML Engineer Salary San Francisco 2026: What the Bay Area Pays

San Francisco has always been the center of gravity for machine learning talent — and in 2026, the gravitational pull is stronger than ever. The concentration of foundation model companies, AI research labs, and Big Tech ML divisions in the Bay Area has pushed compensation to levels unmatched anywhere in the world. But the calculus is more nuanced than the headline numbers suggest.

For a broader view across all major ML markets, see the ML Engineer Salary Guide 2026.

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SF ML Engineer Salary by Level (2026)

Total comp includes base, annual bonus (10–20%), and RSUs vested over 4 years. Foundation model companies (OpenAI, Anthropic) trend toward the upper end at every level.

The defining feature of SF ML compensation in 2026 is the divergence between Big Tech and AI-native companies. At Google or Meta, a senior ML engineer follows a structured L5 band: $180K–$210K base, $35K–$50K bonus, and $200K–$350K in annual RSU grants. At OpenAI or Anthropic, the equity component is structured differently — with profit participation units (PPUs) or preferred equity that carries outsized upside if the company reaches anticipated valuations, but with more illiquidity risk than publicly traded RSUs.

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Top SF Employers for ML Engineers

Foundation model and AI research labs define the top of the market:

OpenAI — The most sought-after employer for LLM researchers and ML engineers. Senior roles command $250K–$350K base with equity that has made early employees paper millionaires. The trade-off: PPUs are illiquid and subject to company performance.

Anthropic — Similar structure to OpenAI. Strong emphasis on interpretability, safety research, and RLHF. Compensation is highly competitive; culture attracts researchers prioritizing long-term AI safety alongside commercial ML work.

Google DeepMind / Google Brain — Post-merger, Google's AI research arm combines DeepMind's research culture with Brain's product integration. Senior and staff engineers earn $350K–$600K+ total comp with fully liquid Google stock (GOOGL).

Meta AI — FAIR (Fundamental AI Research) and the applied ML teams pay comparably to Google. Meta's open-source approach means more published research output; compensation is well-documented via public RSU grants.

Big Tech product ML divisions:

Apple — Notoriously secretive but highly competitive, especially for on-device ML, privacy-preserving inference, and Siri-adjacent work. Senior ML engineers earn $200K–$270K base with strong RSU grants.

Nvidia — GPU architecture and CUDA-adjacent ML roles command premiums. Nvidia's stock appreciation has made RSU grants extraordinarily valuable; senior engineers total $400K–$700K+.

Salesforce — Einstein AI and Slack ML teams. More moderate total comp ($220K–$320K senior) but strong cash-to-equity ratios.

Waymo — Perception, prediction, and planning ML. Senior engineers earn $230K–$380K total comp. Alphabet subsidiary with liquid equity upside.

Lyft — ML platform, pricing models, and ride demand forecasting. Slightly below FAANG ceiling ($200K–$290K senior total comp) but strong ML engineering culture.

Scale AI — ML infrastructure and data quality. Pre-IPO with significant equity upside potential; cash compensation is competitive but equity liquidity is limited until an IPO event.

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Specialization Premiums in SF

Not all ML engineering roles are priced equally. Specialization drives meaningful compensation differences:

The LLM/GenAI premium reflects a genuine supply shortage. The cohort of engineers who understand transformer architectures deeply enough to fine-tune foundation models, implement RLHF pipelines, and deploy inference at scale is still small relative to demand. OpenAI, Anthropic, Google DeepMind, and dozens of well-funded startups compete for the same few hundred genuinely expert practitioners.

MLOps and ML Platform engineering has emerged as the second-highest premium category. As ML systems mature from research to production, companies need engineers who can build reliable training pipelines, feature stores, model serving infrastructure, and evaluation frameworks — skills that are distinct from model research and equally scarce.

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Total Comp Breakdown: Base, RSU, Bonus, and Equity Refreshes

Understanding total compensation at Big Tech requires understanding the full structure:

Base salary is the guaranteed annual cash component. For senior ML engineers at Google, Meta, or Microsoft, this is $180K–$230K. Base is relatively consistent across Big Tech at the same level. Annual bonus (10–20% of base at most companies, higher at some) adds $20K–$46K for senior roles. Bonus is often partially guaranteed in offer letters and partially performance-dependent. RSU grants are the primary differentiator. A senior ML engineer's offer at Google might include $600K–$1.2M in RSUs vesting over 4 years ($150K–$300K/year). These are grants of publicly traded stock — valuable and liquid the moment they vest. Equity refreshes are the key to long-term comp. At Google, Meta, and Amazon, strong performers receive annual refresh grants that replace or exceed the portion of the original grant that has vested. Engineers who stay 5–7 years and perform well often find their annual RSU income growing year over year, making the "4-year vest cliff" model misleading for long-tenured engineers. Foundation model company equity works differently. OpenAI uses profit participation units; Anthropic uses preferred equity. Both carry significant upside if the companies reach $100B+ valuations, but also carry liquidity risk and dilution uncertainty. Engineers at these companies typically receive lower base salaries than Google or Meta to compensate for the equity structure.

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Research Lab vs. Product ML vs. Startup: Comparing the Equity Equation

The choice between Big Tech and AI labs in 2026 is genuinely complex. Big Tech offers certainty: your RSUs are publicly traded, you know their value the day they vest, and refresh grants provide compounding income. AI lab equity carries asymmetric upside — but also asymmetric downside if valuations compress or liquidity events are delayed.

Startup common equity is the highest-risk, potentially highest-reward option. An early ML engineer at a well-positioned AI startup could see 10–100x returns on paper equity at exit, but most startups fail to reach liquidity events, and early employees are often diluted through multiple funding rounds.

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SF vs. Seattle vs. NYC vs. Remote: City Comparison

For a detailed Seattle breakdown, see ML Engineer Salary Seattle 2026. For New York, see ML Engineer Salary New York 2026.

Seattle's competitive advantage is structural: Washington state has no income tax. An ML engineer earning $200K base in Seattle takes home approximately $15K–$25K more annually than an equivalent earner in San Francisco after California taxes. Amazon, Microsoft, and a growing roster of AI-adjacent companies make Seattle's ML market genuinely strong, not merely a fallback from SF.

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Bay Area Cost of Living and Tax Reality

The headline salaries in San Francisco are genuinely impressive. The take-home reality is more sobering.

California income tax: At $200K base, the marginal California state tax rate is 9.3%. Engineers earning $280K+ in total comp hit the 13.3% top rate. Combined with federal taxes (37% marginal above $578K for single filers, 32% for income above $182K), effective total tax rates of 40–45% are common for senior ML engineers. San Francisco rent: A 1-bedroom apartment in desirable SF neighborhoods (Mission, SOMA, Noe Valley) runs $3,200–$4,500/month. A 2-bedroom for couples or roommates: $4,500–$6,500/month. Engineers who move to Oakland or Berkeley save 15–25% on rent with a manageable BART commute. The purchasing power math: An ML engineer earning $210K base and $280K total comp in SF nets approximately $155K–$165K after taxes. After a $3,800/month apartment ($45,600/year), the remaining disposable income is competitive but not extravagant compared to equivalently-paid engineers in Seattle or Austin.

The calculation changes significantly for Big Tech engineers with substantial RSU income. An engineer with $150K–$300K in annual RSU vesting has meaningful wealth accumulation potential even after Bay Area costs — especially if they maximize tax-advantaged accounts and make strategic equity diversification decisions.

For context on how SF ML pay compares to other top-paying roles in tech, see Highest Paying Tech Jobs in 2026.

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Should You Negotiate in San Francisco?

Yes — and the data supports being aggressive. Several factors strengthen ML engineers' negotiating position in SF specifically:

1. Competing offers are expected. Major Bay Area employers anticipate candidates holding multiple offers. Having an offer from Anthropic strengthens your negotiating position at Google, and vice versa. 2. Published compensation data. Levels.fyi, Glassdoor, and the CareerCheck salary database give candidates transparency into real offer ranges. Use this data to anchor your ask at the 75th percentile for your level and specialization. 3. Equity is negotiable. Base salary bands are rigid at L5 and above. RSU grants, signing bonuses, and vesting schedules have more flexibility. Focusing negotiation on the equity component often yields larger dollar improvements than pushing on base. 4. Counter-offers close gaps. If your current employer makes a counter-offer, use it. Bay Area employers know retention is expensive, and many have discretionary equity refresh budgets for high-performing engineers at risk of leaving.

San Francisco's ML engineering market in 2026 remains the most competitive and highest-compensating in the world. The combination of foundation model companies, Big Tech AI divisions, and well-funded AI startups creates genuine competition for proven ML talent. The COL and tax reality means the net financial advantage over Seattle or even remote work is smaller than the gross numbers suggest — but for career trajectory, research exposure, and network effects, the Bay Area's ML ecosystem has no equal.

Benchmark your current ML salary against live SF market data: ML engineer salary data →