Machine Learning Engineer Salary in Boston — 2026 BLS Data
Salary distribution
Percentile breakdown of Machine Learning Engineer base salaries in Boston.
The $152,000 median base salary for a Machine Learning Engineer in Boston is the number you’ll see most cited — and it’s the number that obscures the most. BLS OEWS May 2024 data classifies the bulk of MLE work under Data Scientists (SOC 15-2051) and Software Developers (SOC 15-2052) in Massachusetts, where the Data Scientist statewide median lands at $132,250 and the Software Developer median hits $150,520. Machine Learning Engineers sit at the intersection: more systems-engineering depth than a pure data scientist, more modeling responsibility than a generalist backend developer. The figures above reflect that hybrid positioning, triangulated against Massachusetts OEWS 2024 data and Boston-specific market surveys covering several hundred local reported salaries.
What the median hides is the unusually steep specialization premium in this market. Boston is not a generic tech hub — it is a biotech, life sciences, fintech, and defense-AI corridor anchored by MIT’s Lincoln Laboratory, the Broad Institute, Harvard Medical School affiliates, and financial firms along Atlantic Avenue. An MLE building genomic variant-calling pipelines at a Series C biotech does not occupy the same labor market as an MLE maintaining recommendation systems at a mid-size e-commerce company, even though BLS counts them identically. The gap between those two roles can be $60,000 in base salary alone.
What the median hides: the Boston MLE spread in detail
The P25-to-P90 spread runs from $118,000 to $218,000 — a 85% range that looks wide until you understand what populates each end.
P25 ($118,000) describes an early-career MLE — typically two to four years out of school, often at a biotech startup, a consulting firm, or a mid-market financial services company. This cohort is frequently more data scientist than engineer: they’re comfortable in Python and PyTorch but haven’t yet owned a production ML serving system end-to-end. These roles often carry “Machine Learning Engineer” in the title while paying closer to a senior data analyst. Boston’s large research-hospital network (Mass General, Brigham and Women’s, Dana-Farber) employs a meaningful slice of this cohort at academic-adjacent pay scales that pull the P25 down.
P50 ($152,000) is the mid-level MLE at a growth-stage company or established firm — someone owning a model development lifecycle from feature engineering through deployment, with meaningful production incident history. At this salary point you’re inside a healthy range for a Staff Data Scientist at a large pharma company, a Machine Learning Engineer II at a Series B health-tech startup, or an MLE at a major Boston bank like State Street or Fidelity.
P75 ($182,000) captures senior MLEs and those with specializations — MLOps, reinforcement learning, large language model fine-tuning — that are genuinely scarce in the talent pool. Defense contractors (Raytheon, Draper Laboratory) and hedge funds (Point72, D.E. Shaw’s Boston-area presence) compete in this band. So do biotech firms using ML for drug discovery, where the modeling complexity justifies a premium over generic product-company ML work.
P90 ($218,000) reflects principal-level engineers and highly specialized researchers: RLHF practitioners, robotics ML engineers, those with published NeurIPS or ICML papers who are actively recruited. Boston is home to several AI labs (MIT CSAIL, MIT-IBM Watson AI Lab, Aurora Innovation’s Boston office) where research-adjacent MLE roles land in this range. A small number of Boston-based FAANG satellite offices — Google Cambridge in Kendall Square and Amazon’s AWS Boston outpost — also push into this band.
Hub comparison: how Boston stacks up
Boston’s MLE market is consistently cheaper than San Francisco and roughly on par with New York City for base salary, but the total-comp story diverges significantly once equity is in play.
San Francisco median MLE base is approximately $185,000-$200,000 at the same experience band, with total comp running $250,000-$340,000 at mid-level once equity is included. Boston’s $152,000 median base is about 20-25% below SF. However, Boston’s COL index of 150.8 versus San Francisco’s approximately 178.6 means the gap in purchasing power is meaningfully smaller than the raw salary delta. A $152,000 Boston salary has roughly the same real-dollar spending power as a $179,000 San Francisco salary — so the effective discount you’re taking for Boston is closer to 10-15% in purchasing power terms, not 20-25%.
New York City runs $165,000-$180,000 median base for mid-level MLEs, narrowly ahead of Boston. The NYC premium is driven by finance-sector demand — Goldman Sachs, two-sigma, and Jane Street pay FAANG-comparable cash compensation for quantitative ML roles. Boston’s equivalent pipeline (Fidelity, State Street, Wellington Management) is large but pays somewhat more conservatively on cash, partly offset by more modest cost of living.
Seattle is roughly comparable to Boston on base salary in the $155,000-$170,000 range, but Amazon’s back-loaded RSU structure (5-15-40-40 vesting) inflates year-3 and year-4 total comp in ways that don’t show up in Boston packages.
Austin trails Boston by $20,000-$30,000 on MLE base, consistent with its position as a lower-cost hub. The purchasing-power gap narrows substantially — Austin’s COL index of about 119 versus Boston’s 150.8 makes a $125,000 Austin package roughly equivalent in spending power to a $158,000 Boston package.
What drives the spread: company tier, level, and specialty
Three variables explain most of the P25-to-P90 variance within Boston, independent of years of experience.
Company tier is the single biggest lever. Boston’s market breaks into roughly four tiers: (1) FAANG satellite offices and AI labs — Google Cambridge, Amazon AWS, MIT-IBM Watson AI Lab — paying $185,000-$218,000 base at mid-level; (2) late-stage biotech and pharma (Moderna, Biogen, AstraZeneca’s US headquarters in Cambridge) at $155,000-$180,000; (3) well-funded growth startups and financial firms (Klaviyo, HubSpot, State Street Global Advisors, Fidelity) at $140,000-$170,000; (4) early-stage startups and academic spinouts at $110,000-$140,000 with equity offsetting the base discount.
Level and scope matters as much in Boston as anywhere. An MLE I (individual contributor, two to three years) typically earns $118,000-$138,000. MLE II (owns a model pipeline, mentors interns): $140,000-$165,000. Senior MLE (design-level ownership, cross-team influence): $165,000-$195,000. Staff or Principal MLE (sets technical direction for a domain): $195,000-$230,000+. These bands compress slightly relative to FAANG because fewer Boston employers have as many levels or as much equity to fill the gaps with.
Specialty is where BLS data shows its biggest blind spot. General ML (classification, regression, deployed model serving) commands the baseline. MLOps and ML platform engineering adds a 10-15% premium because Boston companies are racing to industrialize ML workflows and there are far fewer MLOps specialists than modelers. LLM fine-tuning, retrieval-augmented generation (RAG) system design, and RLHF expertise command another 15-25% premium in 2026. Computational biology and genomics ML — applying deep learning to protein structure prediction, variant calling, or drug-target interaction — sit in their own market segment at Boston’s biotech cluster, often paying at the high end of tier-2 ranges because the talent pool of people who can do both wet-lab biology and production ML is genuinely small.
Total compensation breakdown
For a mid-level MLE (P50 base, $152,000) at a typical Boston growth-stage company or established firm, the full package looks like this:
Base salary: $152,000. This is what BLS tracks. Bands are generally not public in Massachusetts (unlike California, which mandates posting), but recruiter conversations will tell you the range is typically ±$10,000-$15,000 around midpoint. Base is the hardest number to move in negotiation.
Annual bonus: $18,000. Most non-FAANG Boston employers structure this as a 10-15% target bonus paid annually, tied to company performance and individual rating. Financial firms (Fidelity, State Street) lean toward the high end at 15-20%. Biotech startups below Series C often skip cash bonuses entirely, substituting additional equity.
Annualized equity: $22,000. This is where Boston diverges from coastal tech hubs most sharply. A mid-level MLE at a Boston growth company might receive a four-year RSU grant worth $80,000-$100,000 at grant date — annualized that’s $20,000-$25,000, far below the $60,000-$80,000 annualized equity a comparable MLE might see at a FAANG. Biotech employers frequently issue stock options rather than RSUs, which complicates comparison. Defense contractors rarely offer equity at all, which is why they pay at the higher end of cash ranges to compensate.
Total: approximately $192,000. For FAANG satellite offices and top-paying Boston AI labs, total comp at this experience level runs $240,000-$280,000 — the difference is almost entirely in equity. Pre-IPO startup equity could, in theory, eclipse all of it; practically speaking, fewer than one in five Boston ML engineering exits result in meaningful liquidity for employee 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. Housing is the dominant driver: median one-bedroom rent in Boston proper is approximately $2,900-$3,200/month in 2025, compared with roughly $1,400 nationally. A $152,000 Boston salary, COL-adjusted, has the purchasing power of approximately $100,800 at the US average — or roughly $120,000 in Austin.
The practical implication: a Boston MLE earning P50 ($152,000) is comfortably middle-class by national standards but is not accumulating wealth quickly unless they have a partner with income or have bought property before the 2020-2023 run-up. At P75 ($182,000), the picture improves substantially — housing is still expensive but savings rates become meaningful, especially in lower-cost Boston neighborhoods (Jamaica Plain, Roslindale, East Boston) or inner suburbs (Somerville, Medford, Arlington).
The COL-adjusted comparison to San Francisco is interesting: Boston’s $152,000 at COL 150.8 has essentially the same purchasing power as $179,000 in San Francisco at COL 178.6. Given that SF median MLE base is approximately $185,000-$195,000, the real take-home advantage of SF over Boston is roughly $5,000-$16,000 annually — meaningful but much smaller than the raw salary delta suggests. The counterargument for Boston: state income tax is 9% in California versus 5% on income over $1 million in Massachusetts (5% flat below that threshold), which further compresses the net-of-tax gap.
Where the COL model breaks down: housing costs are geographically irreversible. An MLE who bought a Cambridge condo in 2018 has a fixed housing cost that COL indices cannot capture. An MLE renting in Seaport pays two-thirds of the benefit of the P50-to-P75 salary jump straight to a landlord. The COL-adjusted numbers assume you face average costs, which is not true for recent arrivals in Boston’s tightest neighborhoods.
Three-lever negotiation playbook
Boston’s MLE market has specific dynamics that reward preparation. These three levers, in order of impact:
1. Specialization arbitrage: name the premium, then name the number. Generic “ML engineer” candidates accept generic ML engineer offers. If your specific background — LLM alignment, clinical NLP, genomics deep learning, quantitative portfolio ML, MLOps platform architecture — maps to something Boston employers are actively struggling to hire, say that explicitly and early. “I’ve spent the last three years doing end-to-end RAG system design for regulated healthcare environments; I know there are only a handful of people in Boston who’ve done that at scale” is not a boast, it is market information. It shifts the negotiation from “what is the band” to “what is this specific skill worth.” Specialization premiums of 15-25% over generic band midpoints are realistic and documented.
2. Use competing sector offers, not just competing role offers. Boston is unusual in having parallel demand for ML talent across biotech, fintech, defense, and product tech — sectors with meaningfully different compensation philosophies. A competing offer from a hedge fund or defense contractor (cash-heavy, equity-light) compared against a biotech offer (equity-heavy, cash-moderate) creates leverage even though the headline numbers may be similar, because the risk-adjusted value differs. Document the full comp picture of both offers, then ask the preferred employer which levers they can move. A biotech firm that cannot move base may be able to add a milestone bonus tied to a drug-development stage event, for example.
3. Negotiate the equity structure, not just the quantum. Many Boston employers — especially pre-IPO biotech — offer options with long exercise windows (sometimes five to seven years post-departure, rather than the standard 90-day window). Ask explicitly for an extended post-termination exercise period if the company is more than three years from a likely liquidity event. Early-exercise rights (Section 83(b) election eligibility) on options with a low strike price can convert a nominal equity package into a significant tax advantage. These are negotiable at most Boston startups if you ask during the offer stage; almost no one asks, so almost no one gets them.
Sign-on bonuses are less universal in Boston than in SF, but they exist and are recruiter-discretionary. Typical range: $15,000-$40,000 at mid-level, $40,000-$75,000 at senior-to-staff. If you are walking away from unvested equity at a previous employer, document the unvested amount and ask for a make-whole payment — this is standard practice at FAANG and increasingly at Boston growth companies.
Data caveats
The figures in this page are BLS-grounded but extrapolated, because BLS OEWS does not publish a separate “Machine Learning Engineer” SOC code. The percentile figures are derived from BLS OEWS May 2024 data for Massachusetts Data Scientists (15-2051, median $132,250) and Massachusetts Software Developers (15-1252, median $150,520), weighted toward the software developer distribution because production MLE work is engineering-intensive, then cross-referenced against Boston-specific market data from Built In Boston (median base $162,000-$163,000), ZipRecruiter (P25 $110,300, P75 $168,400, P90 $193,379), and Indeed (average $189,869 as of mid-2026). The BLS anchor is more conservative than market surveys because it captures the full population of relevant workers — including lower-paid academic and nonprofit positions — whereas job-posting aggregators index toward active market participants.
Equity is excluded from BLS tracking entirely. For FAANG satellite offices and well-funded AI labs, this understates total compensation by 30-50% at senior levels. For biotech-heavy paths or pre-IPO startups, the relationship between equity and actual realized value is too speculative to include in a base salary comparison.
The May 2024 OEWS data reflects wages from 2024; by mid-2026, AI/ML specialization premiums have continued to expand as demand outpaces supply of engineers with production experience. The figures above should be treated as a floor for experienced candidates, not a ceiling for negotiation.
For real-time benchmarking, supplement BLS data with Levels.fyi (Greater Boston median MLE total comp approximately $197,500 across reported data points), current Massachusetts salary ranges posted under PFML-adjacent transparency norms, and your recruiter’s direct statement of the band. The triangulation of BLS base, Levels total comp, and band midpoint gets you within 8-12% of a defensible number before you walk into the offer conversation.