AI Engineer Salary in Boston — 2026 BLS Data
Salary distribution
Percentile breakdown of AI Engineer base salaries in Boston.
The $157,000 median base salary for an AI Engineer in Boston is a number that demands context before it means anything useful to you. BLS OEWS May 2024 data for Massachusetts places Software Developers (SOC 15-1252) at a statewide median of $150,520 and Data Scientists (SOC 15-2051) at $132,250 — AI Engineers sit above both benchmarks because the role demands production engineering depth plus modeling expertise, a combination that is genuinely scarce. The figures on this page are triangulated from those BLS state anchors, Boston-metro market surveys, and the structural characteristics of Boston’s AI hiring market: a biotech corridor, a defense cluster, a growing LLM application layer, and a handful of major research labs, all competing for the same narrow talent pool.
The median hides two things that matter. First, the spread is dramatic: P25 to P90 covers a $118,000 range, nearly 75% of the median itself. Second, that spread is not random noise — it is almost entirely explained by three variables (company tier, specialization, and level) that you can directly influence. The rest of this page breaks down both.
What the $157K median hides: the P25–P90 spread explained
P25 ($122,000) describes an early-career AI Engineer, typically two to three years out of school, working in a role that is functionally closer to applied data science than to production AI systems work. This cohort is competent in Python and a modern ML framework — PyTorch, Hugging Face Transformers, or LangChain — but has not owned a serving system in production at meaningful scale. At this salary level you’ll find: AI Engineers at Series A startups where the title is aspirational, “AI Developer” roles at mid-market companies integrating third-party model APIs without building their own, and academic-adjacent positions at Boston’s research hospitals (Mass General Brigham, Dana-Farber, Beth Israel Deaconess), which set pay against academic postdoc scales that depress the bottom quartile city-wide.
P50 ($157,000) is the mid-level AI Engineer who owns something end-to-end — a RAG pipeline serving real users, a fine-tuned model deployed to production, an LLM evaluation framework that the team actually uses to gate releases. At this salary point you’re in healthy territory for an AI/ML Engineer II at a Series B or C company, a machine learning engineer at Fidelity Investments or State Street, or an AI specialist at one of Boston’s many biotech firms using ML for protein structure prediction or clinical trial optimization.
P75 ($198,000) captures senior-level engineers and those with specific scarcities: LLM alignment and RLHF practitioners, clinical NLP engineers with HIPAA production experience, or AI platform engineers who can build the infrastructure that other ML teams run on top of. At this salary level you’re in competition with defense contractors — Raytheon Technologies, Draper Laboratory, MIT Lincoln Laboratory — and the established hedge fund shops (Acadian Asset Management, Panagora Asset Management) that compete hard for quantitative AI talent with cash-heavy packages.
P90 ($240,000) reflects principal AI engineers and highly specialized researchers: people with NeurIPS or ICLR publications who are being recruited directly from academia, staff-level engineers at FAANG satellite offices in Kendall Square, and senior practitioners at AI-native labs. Google’s Cambridge office in Kendall Square, Amazon’s AWS Boston engineering presence, and the MIT-IBM Watson AI Lab all pull candidates into this range. A small but growing cohort of AI Agents and applied reasoning engineers — working on multi-step planning systems for biotech or defense applications — also appears at the P90 ceiling.
Hub comparison: how Boston stacks up
Boston’s AI Engineer market is the fourth-largest in the US by headcount behind San Francisco, New York, and Seattle, but it is structurally distinct from all three.
San Francisco still anchors the top of the national market. An AI Engineer at a FAANG or frontier lab (Anthropic, OpenAI, Google DeepMind) in SF earns $200,000–$250,000 in base alone at mid-level, with total comp frequently exceeding $350,000 once RSUs are included. Boston’s $157,000 median base is roughly 25–30% below SF on cash. However, Boston’s COL index of 150.8 versus San Francisco’s approximately 178.6 means the purchasing-power gap is closer to 12–14%. A $157,000 Boston salary has roughly the same real buying power as a $185,000 San Francisco salary — so the effective discount you’re taking by staying in Boston is meaningful but far smaller than the gross salary delta suggests.
New York City runs $170,000–$190,000 median base for AI Engineers, narrowly ahead of Boston. NYC’s premium reflects finance-sector demand — Goldman Sachs, Citadel, Two Sigma, and Jane Street all run large AI engineering teams and pay near-FAANG on cash compensation. Boston’s fintech and asset management sector (Fidelity, State Street, Wellington Management) is comparable in scale but somewhat more conservative on base salary for AI roles, typically 8–12% below comparable NYC finance-sector offers.
Seattle is roughly Boston’s peer on AI Engineer base salary, with a $155,000–$175,000 median range. Amazon’s back-loaded RSU vesting schedule (5-15-40-40 over four years) inflates Seattle’s year-3 and year-4 total comp significantly; a Boston AI Engineer at a non-Amazon employer needs to account for that asymmetry when comparing offers.
Austin trails Boston by $25,000–$35,000 in AI Engineer base, consistent with its position as a lower-cost tech hub. Austin’s COL index of roughly 119 (versus Boston’s 150.8) narrows the purchasing-power gap considerably — an $130,000 Austin AI Engineer salary has approximately the same real-dollar spending power as a $164,000 Boston salary.
The case for staying in Boston versus relocating to SF comes down to what you value beyond the paycheck. Boston is where life sciences AI is happening: Moderna, Biogen, Sanofi, and Vertex Pharmaceuticals are all building serious AI engineering teams within a 10-mile radius of Kendall Square. If you want to work on drug discovery, clinical genomics, or healthcare AI in a production environment, Boston is the deepest market in the world for that work, and the salary discount to SF is real but manageable.
What drives the spread: company tier, level, and specialty
Three variables account for most of the P25-to-P90 variance within Boston, independent of raw years of experience.
Company tier is the single largest lever. Boston’s AI engineering market breaks into roughly four tiers. First tier: FAANG satellite offices and top AI research labs — Google Cambridge, Amazon AWS Boston, MIT-IBM Watson AI Lab — paying $195,000–$240,000 base at mid-level. Second tier: late-stage biotech and pharma with established AI programs — Moderna, Biogen, AstraZeneca’s US headquarters in Cambridge, Vertex Pharmaceuticals — at $165,000–$195,000. Third tier: well-funded growth companies and financial firms — HubSpot, Klaviyo, Fidelity, State Street Global Advisors, Accenture Federal Services — at $145,000–$175,000. Fourth tier: early-stage AI startups and academic spinouts at $115,000–$145,000, where equity is offered as partial compensation for the base discount.
Specialization is where the BLS numbers show their biggest blind spot. General AI Engineering — prompt engineering, RAG architecture, LLM API integration — commands the baseline. MLOps and AI platform engineering (designing the infrastructure other ML teams deploy on) adds a 12–18% premium because Boston companies are racing to productionize AI workflows and the supply of engineers who can do this at scale is thin. LLM fine-tuning and RLHF expertise adds another 15–20% over the generalist baseline in 2026. Computational biology AI — applying deep learning to protein folding, variant calling, single-cell RNA sequencing, or drug-target interaction prediction — sits in its own market segment, frequently paying at the top of tier-two ranges because the population of engineers who can do both genomics and production AI is genuinely rare.
Level and scope matters as much in Boston as anywhere. An AI Engineer I (individual contributor, one to three years) earns $122,000–$142,000. AI Engineer II (owns a pipeline or system, works independently): $145,000–$170,000. Senior AI Engineer (design ownership, cross-functional technical leadership): $170,000–$205,000. Staff or Principal AI Engineer (sets direction for an AI platform or discipline): $205,000–$245,000+. These bands compress relative to FAANG because fewer Boston employers have six distinct engineering levels or RSU refresh budgets large enough to fill the gaps with equity.
The LLM-application premium is real, but narrowing
In 2024, AI Engineers with production experience deploying large language model applications — including agentic workflows, retrieval-augmented generation, and multi-model orchestration — commanded a 20–30% premium over generalist ML engineers at the same level. That premium was approximately 15–20% by mid-2026, as more engineers accumulated relevant experience and employer tooling (LangChain, LlamaIndex, Vertex AI, Bedrock) made the implementation layer more accessible. The premium has not disappeared, but it has concentrated at the top end: engineers who can design and defend the evaluation framework, not just the application, continue to earn the full premium.
Total compensation breakdown
For a mid-level AI Engineer (P50 base, $157,000) at a typical Boston growth-stage company or established tech firm, the full package looks like this:
Base salary: $157,000. This is the number BLS OEWS tracks and the number your W-2 will report. Massachusetts does not mandate salary range disclosure on job postings (unlike California), but recruiter conversations will typically reveal a band of $140,000–$175,000 for this experience tier. Base is the hardest component to move in negotiation; bands require VP approval to exceed at most companies.
Annual bonus: $19,000. Most non-FAANG Boston employers structure this as a 10–15% target bonus paid annually, contingent on company financial performance and individual performance rating. Financial services firms (Fidelity, State Street) lean toward 15% or higher. Biotech startups pre-Series C frequently skip cash bonuses entirely, substituting additional equity. Defense contractors pay bonuses but often defer to government contract billing structures that make performance bonuses smaller and less predictable.
Annualized equity: $25,000. This is the most variable component and the one where Boston diverges most from coastal hubs. A mid-level AI Engineer at a Boston growth company might receive an initial RSU grant worth $90,000–$110,000 vesting over four years — annualized, that is $22,000–$27,000. Compare this to the $80,000–$120,000 annualized equity a comparable engineer might see at a FAANG or frontier AI lab in SF. Biotech employers frequently issue stock options rather than RSUs, which adds both tax complexity and execution risk. Defense contractors rarely offer equity of any kind, compensating with higher base salary.
Total: approximately $201,000. For FAANG satellite offices and well-capitalized AI labs in Boston, total comp at this experience level runs $260,000–$320,000 — the delta is almost entirely equity. Pre-IPO biotech equity could, in theory, exceed all of that; in practice, the median biotech employee does not see meaningful liquidity from options, and fewer than one in five venture-backed biotech companies reach an exit that produces significant returns for non-founder equity holders.
Cost-of-living adjusted perspective
Boston’s COL index of 150.8 means total living costs run 50.8% above the US national average, placing it solidly in the same tier as Chicago (approximately 108) and below New York City (approximately 187) and San Francisco (approximately 178.6). Housing is the dominant driver: median one-bedroom rent in Boston proper is approximately $3,000–$3,300 per month in 2026, versus roughly $1,450 nationally. A $157,000 Boston AI Engineer base, COL-adjusted, has the purchasing power of approximately $104,100 at the US average — enough to live comfortably in most of the country, but not enough to build meaningful savings quickly while paying Boston rents unless you have a partner with income.
The COL-adjusted comparison across hubs is revealing. At P50, Boston’s $157,000 at COL 150.8 has essentially the same purchasing power as approximately $185,000 in San Francisco at COL 178.6 — which is close to SF’s median AI Engineer base. The real take-home advantage of SF over Boston is narrower than the nominal salary gap implies, and that gap shrinks further when you factor in Massachusetts income tax (5% flat rate below the $1M surtax threshold) versus California’s 9.3% marginal rate at this income level.
At P75 ($198,000), the picture improves substantially. With disciplined renting in lower-cost neighborhoods — Jamaica Plain, Roslindale, East Boston, or the inner suburbs of Somerville, Medford, or Arlington — a $198,000 base allows for meaningful savings accumulation. The Boston neighborhoods surrounding the biotech and tech corridors (Kendall Square, Seaport, Back Bay) remain expensive; the 20-minute commute neighborhoods are significantly more affordable and accessible by T.
Where the COL model underestimates actual costs: childcare in Boston runs $3,500–$4,500 per month for infant care as of 2025, among the highest in the country. A dual-income household with one child can easily spend $42,000–$54,000 annually on childcare alone, a cost not fully reflected in composite COL indices that weight housing most heavily.
Three-lever negotiation playbook
Boston’s AI engineering market has specific dynamics that reward preparation over generic negotiation scripts.
1. Name your specialization and connect it to a specific pain point. The single most effective negotiation move in Boston’s AI market is framing your background as a solution to a specific scarcity, not as a generic credential. “I’ve spent three years building RAG pipelines for FDA-regulated clinical trial data — I know the specific privacy constraints, the auditability requirements, and the model evaluation standards that apply in that environment” is worth more than “I have five years of ML experience.” It is not a brag; it is market information that justifies a deviation from band midpoint. Boston hiring managers at biotech, healthcare AI, and defense companies will respond to domain specificity in a way that pure product-tech hiring managers often do not, because they are solving for scarcity in a specialized talent pool. Specialization premiums of 15–25% over generic band midpoints are documented and achievable.
2. Use cross-sector competing offers, not just same-sector competing offers. Boston is rare in having simultaneous demand for AI engineers across biotech, fintech, defense, and product technology — four sectors with meaningfully different compensation philosophies. A competing offer from a defense contractor (cash-heavy, no equity) compared to a biotech offer (equity-heavy, moderate cash) creates negotiating leverage even if the headline numbers are similar, because the risk-adjusted value of those packages differs. Present both offers fully to your preferred employer and ask explicitly which components they can address: “I prefer this role, but the total value of the defense offer is $X. Can you address that gap in signing bonus or equity?” Finance-sector and defense offers are particularly useful as anchors because their cash compensation is consistently higher than startup cash, and most AI companies would rather close their gap than lose a candidate to a sector they don’t typically compete with.
3. Negotiate equity structure alongside equity quantum. Many Boston employers — particularly pre-IPO biotech — offer options with a standard 90-day post-termination exercise window. That window is effectively a forfeiture mechanism for anyone who cannot afford to exercise options after leaving. Ask explicitly for an extended post-termination exercise period of two to five years; many Boston biotech companies will grant this if asked during offer negotiation, because it costs nothing until a liquidity event and almost no candidates ask for it. If options have a low strike price, ask about early-exercise rights and whether the company supports Section 83(b) elections — filing within 30 days of an early exercise can convert appreciation into long-term capital gains rather than ordinary income, a material tax difference at exit. These structural terms are negotiable at most Boston AI startups during the offer stage; they are standard knowledge in SF and nearly invisible in Boston, which is precisely why asking gives you an edge.
Sign-on bonuses exist in Boston but are less universal than in SF. Typical range: $15,000–$35,000 at mid-level, $40,000–$70,000 at senior-to-staff. If you are walking away from unvested equity at a current employer, document the unvested amount and request a make-whole payment. This is standard practice at FAANG and increasingly at Boston growth companies — many have explicit policies for it — but you must ask.
Data caveats
BLS OEWS is the most rigorous public wage source — mandated survey covering tens of millions of workers — but it does not publish a standalone “AI Engineer” SOC code. The occupation closest to this role in 2024 is split across Software Developers (15-1252, Massachusetts median $150,520) and Data Scientists (15-2051, Massachusetts median $132,250), with AI Engineers occupying the high end of the software developer distribution. The percentile figures on this page are derived by anchoring to those BLS state-level figures, applying Boston metro premium factors consistent with the BLS Boston-Cambridge-Nashua area wage data for computer occupations (where the metro median runs approximately 8–12% above the Massachusetts state median), and cross-referencing against Boston-specific market surveys: Glassdoor reports Boston AI Engineer P25 at $121,794 and P75 at $193,264 as of 2026; ZipRecruiter places the Boston AI Engineer average at $163,135; Randstad reports $163,135 average for artificial intelligence engineers in Boston. The BLS anchor is consistently more conservative than these market surveys because it captures the full working population — including lower-paid academic, nonprofit, and government positions — whereas job-posting aggregators index toward active market participants.
Equity is excluded from BLS tracking entirely. For FAANG satellite offices and well-capitalized AI labs, this understates total compensation by 30–50% at senior levels. For biotech options, the relationship between grant-date value and realized value is too speculative to include in a base salary comparison.
The May 2024 OEWS data captures wages paid during a period when the LLM-application layer was maturing rapidly. By mid-2026, AI engineering specialization premiums — particularly for agents, evaluation frameworks, and production inference optimization — have continued to expand as commercial deployment outpaces supply of engineers with relevant production experience. Treat the percentile figures above as a well-grounded baseline, not as a ceiling. For real-time verification, supplement with Levels.fyi Greater Boston AI/ML engineer data (median total comp approximately $194,000 across reported data points), current Massachusetts job postings, and the explicit salary band your recruiter discloses. Triangulating these three sources gets you within 8–12% of a defensible number before you sit down to negotiate.