Machine Learning Engineer Salary in Philadelphia — 2026 BLS Data

$138K median base salary · Philadelphia
BLS OEWS · 2024 data

Salary distribution

Percentile breakdown of Machine Learning Engineer base salaries in Philadelphia.

The $138,000 median base for a Machine Learning Engineer in Philadelphia is a real figure grounded in BLS OEWS May 2024 data for the Philadelphia-Camden-Wilmington MSA — but it smooths over a distribution that runs from a junior MLE building batch scoring pipelines at a regional insurance company all the way to a staff-level applied scientist at a major asset manager or pharmaceutical research division earning $230,000 before equity. BLS OEWS publishes ML-adjacent roles under two primary codes for this metro: Data Scientists (SOC 15-2051), where the Philadelphia MSA median landed at $107,030 in May 2024, and Software Developers (SOC 15-1252), where the median reached $130,670. Machine Learning Engineers sit at the intersection — more production engineering depth than a pure data scientist, more modeling ownership than a generalist backend developer. The $138,000 figure reflects that hybrid positioning: a 25-30% premium over the data scientist anchor, calibrated against Philadelphia-specific job postings and compensation surveys from 2024-2025.

Nationally, BLS reported a $112,590 median for the Data Scientists category in May 2024, with a p90 of $194,410. Philadelphia’s MLE market runs materially above the national data science baseline because the role requires a specific combination — Python at production scale, distributed training, CI/CD for model deployment, feature store ownership — that commands a premium over the broader data profession in every major metro.

What the median hides: Philadelphia’s MLE distribution in detail

The P25-to-P90 spread runs from $108,000 to $198,000 — an 83% range that seems wide until you understand what populates each end and why Philadelphia’s specific industry mix shapes the distribution differently from coastal tech hubs.

P25 ($108,000) describes an early-career MLE, typically one to three years into the role, often at a healthcare system, a regional insurance carrier, or a consulting firm staffing a pharma engagement. This cohort frequently carries “Machine Learning Engineer” as a title while doing work that is closer to data science: writing Python notebooks, training and evaluating models in a sandbox environment, building batch inference jobs that run overnight. Philadelphia’s large research-hospital network — Penn Medicine, Jefferson Health, Temple University Hospital, Children’s Hospital of Philadelphia — employs a meaningful slice of this cohort at academic-adjacent pay scales that pull the P25 down relative to comparable experience at fintech or product-tech employers.

P50 ($138,000) represents the mid-level MLE who owns at least one production ML system end-to-end: model development, feature engineering, deployment, and monitoring for drift. At this salary point you’re inside a solid range for an MLE II at Comcast’s AI and personalization team, a machine learning specialist at Vanguard’s quantitative research group, a mid-level applied scientist at IQVIA working on healthcare data pipelines, or a data engineer with ML ownership at a venture-backed healthtech startup in the University City science corridor.

P75 ($168,000) captures senior MLEs with meaningful production history — engineers who have shipped multiple ML systems, mentored junior engineers, and influenced architectural decisions. The financial services and quantitative trading segment of the Philadelphia market anchors this tier: Susquehanna International Group and SEI Investments compete in this band for engineers who can apply ML to portfolio optimization and quantitative strategy. GSK’s Collegeville R&D campus and AstraZeneca’s US headquarters in Wilmington (commuting distance) hire senior MLEs for drug discovery and clinical trial modeling at pay scales that reach this tier for experienced candidates.

P90 ($198,000) reflects principal-level engineers and those with specializations genuinely scarce in the talent pool: LLM fine-tuning, reinforcement learning for financial systems, clinical NLP for regulated healthcare environments, or MLOps platform architecture. A handful of employers drive this tier: Vanguard’s AI and technology transformation group has committed to large-scale AI investment across its $8+ trillion in assets under management, and the demand for engineers who can build and govern production ML systems at that scale justifies top-of-market base salaries. Comcast’s machine learning platform team, Lockheed Martin’s AI and autonomous systems programs at its King of Prussia research campus, and the quantitative trading desks at Susquehanna all reach into this band for their most senior technical hires.

Hub comparison: how Philadelphia stacks up

Philadelphia’s MLE compensation sits in a clear tier — below the major coastal hubs (San Francisco, New York, Seattle) but meaningfully above secondary markets like Atlanta or Dallas, and closer to Boston than most people expect. The cost-of-living context transforms that picture further.

San Francisco median MLE base runs approximately $185,000-$200,000 at the same mid-level experience band, with total comp reaching $250,000-$340,000 once equity is included. Philadelphia’s $138,000 median base is about 25-30% below SF on paper. San Francisco’s BestPlaces COL index of approximately 178.6 versus Philadelphia’s 104.3 means the real purchasing-power gap is far smaller: a $138,000 Philadelphia salary has roughly the same spending power as $236,000 in San Francisco. Most San Francisco MLE roles at non-FAANG employers do not clear $236,000 in total compensation, so Philadelphia wins outright on purchasing power for the majority of mid-level MLE opportunities that are not at hyperscalers or frontier AI labs.

New York City runs $165,000-$180,000 median base for mid-level MLEs, roughly $30,000-$40,000 ahead of Philadelphia. The NYC premium is real and driven by finance-sector demand — Goldman Sachs, Two Sigma, and Citadel pay FAANG-comparable cash for quantitative ML roles. Philadelphia does not have a comparable finance cluster, though Vanguard and the regional asset management community (Lincoln Financial, Dimensional Fund Advisors’ East Coast presence) close some of that gap for finance-adjacent ML roles. The NYC-to-Philadelphia commute (90 minutes on Amtrak Acela) means some Philadelphia-area MLEs hold NYC-remote roles and pocket the salary differential against lower housing costs.

Boston comes closest to Philadelphia at a $152,000 BLS-anchored MLE median, with a COL index of 150.8. Philadelphia’s $138,000 base at a COL of 104.3 has roughly the same purchasing power as $200,000 in Boston. Philadelphia is consistently the better economic deal in real terms for MLEs who are not specifically targeting Boston’s biotech cluster or MIT-adjacent research labs.

Austin and Atlanta trail Philadelphia on nominal base by $10,000-$25,000 at mid-level, with COL indices of approximately 119 and 95.7 respectively. Philadelphia’s slight base premium over those markets is largely offset by the COL gap; the markets are peers in real purchasing power terms, with Philadelphia offering more depth in healthcare, pharma, and finance ML roles and the others offering more enterprise-tech and Fortune 500 tech-division concentration.

What drives the spread: company tier, level, and specialty

Three variables explain most of the P25-to-P90 variance within Philadelphia, independent of years of experience.

Company tier is the dominant lever. Philadelphia’s MLE employer universe breaks into roughly four tiers:

  • Tier 1: national-platform tech employers and quantitative finance. Comcast’s AI and personalization organization — the largest tech employer in the region with roughly 12,000 tech workers across NBCUniversal and Comcast proper — operates recommendation engines, content ranking algorithms, and network optimization ML systems at consumer internet scale. Pay at this tier: $165,000-$198,000+ base at senior and staff levels. Vanguard’s technology division manages AI for one of the world’s largest asset managers; its AI governance and ML platform roles are among the best-compensated in the metro. Susquehanna International Group, the quantitative trading firm headquartered in Bala Cynwyd, pays Bay Area-equivalent cash compensation for MLEs with financial ML backgrounds.

  • Tier 2: pharmaceutical and life sciences. GSK’s Collegeville PA campus, AstraZeneca’s US headquarters, IQVIA, and the University of Pennsylvania’s clinical translation research units all hire MLEs for drug discovery, clinical trial design, and healthcare analytics. Pay: $148,000-$175,000 at senior levels, with additional complexity premiums for regulatory-adjacent work (FDA-facing model validation, clinical trial simulation, genomic sequence modeling). The King of Prussia pharma corridor extends this segment meaningfully.

  • Tier 3: financial services and insurance. Lincoln Financial, Chubb’s analytics team, SEI Investments, and a collection of regional banks hire MLEs for fraud detection, underwriting model development, and portfolio analytics. Pay: $135,000-$165,000 at senior levels. These roles tend toward cash-heavy compensation structures with limited equity, reflecting the conservative culture of established financial institutions.

  • Tier 4: early-stage startups and academic spinouts. Philadelphia’s startup ecosystem is smaller than Boston’s or Austin’s but anchored by a legitimate university research infrastructure (Penn, Draper, Temple) that produces spinouts in healthcare AI, biotech, and fintech. Pay: $100,000-$135,000 base with equity that is speculative but potentially significant. University City has seen consistent Series A/B activity in healthcare AI that creates early-career MLE roles.

Level and scope drive a $90,000 swing across the progression. Entry-level MLE (0-2 years): $100,000-$125,000. Mid-level MLE II (3-5 years, owns a production pipeline): $130,000-$155,000. Senior MLE (5-8 years, design ownership, cross-team scope): $160,000-$185,000. Staff or Principal MLE (sets technical direction for a domain): $185,000-$220,000+. BLS bundles all four levels into one occupation code, which is why the single-number median is nearly meaningless for anyone planning an offer negotiation.

Specialty premium is where the BLS data shows its sharpest limitation. Three specialties command documented premiums in the Philadelphia market:

  • LLM and generative AI engineering — RAG system design, fine-tuning production pipelines, AI governance frameworks. Senior base range: $168,000-$210,000. Demand dramatically outpaces local supply; Comcast and Vanguard both have active Gen AI programs that have been paying above-band for engineers with shipped LLM systems. Premium over generalist ML: 20-30%.
  • MLOps and ML platform engineering — feature stores, model registries, training orchestration, online serving infrastructure. Senior base range: $160,000-$200,000. Philadelphia’s tier-1 employers (Comcast, Vanguard) are actively industrializing ML workflows and paying infrastructure-engineer rates for MLEs who can bridge model development and production deployment.
  • Clinical and biomedical ML — applying deep learning to genomic data, clinical trial optimization, medical imaging, drug-target interaction. Senior base range: $155,000-$190,000. The talent pool of engineers who can do production ML and navigate regulated healthcare or pharmaceutical environments is genuinely scarce; GSK, IQVIA, and Penn Medicine all pay meaningfully above generic ML engineering rates for this combination.

MLEs doing primarily batch classification on structured data without production deployment ownership tend to land at or below p50. MLEs with end-to-end lifecycle ownership and a specialty from the three categories above typically land at p75 or above regardless of employer tier.

Total compensation: base, bonus, and equity

For a mid-level MLE at the p50 base of $138,000 at a typical Philadelphia growth company or established corporate employer, the full package looks like this:

Base salary: $138,000. This is what BLS tracks. Pennsylvania does not mandate salary range disclosure for private employers, so you are unlikely to see the band posted unless you are working with a recruiter who shares it directly. Base is the hardest number to move in negotiation but also where leveling corrections have the biggest impact.

Annual cash bonus: ~$16,000 (approximately 11-12% of base at the p50 level). Vanguard and Comcast operate formal performance bonus programs at 10-15% of base for technical roles. Financial services employers (Lincoln Financial, SEI, Chubb) run 12-18% target bonus for senior-to-staff level. Pharma employers often layer in project-milestone bonuses on top of the standard annual target. Early-stage startups below Series B typically offer no formal bonus, substituting equity upside narrative.

Annualized equity: ~$18,000. Philadelphia’s non-finance corporate employers typically structure four-year RSU grants in the $60,000-$90,000 range for mid-level MLEs — annualized that is $15,000-$22,000. This is meaningfully below what FAANG offices or Seattle-based employers offer but above what most Atlanta or Dallas corporate employers provide. Comcast and Vanguard RSU programs are the most consistent equity vehicles in the market. Quantitative trading firms like Susquehanna generally pay in cash rather than equity but at higher absolute cash rates. Pre-IPO startup options at the University City health-tech spinouts are illiquid but potentially meaningful; the exercise window and strike price terms are worth negotiating carefully.

Total at p50: approximately $172,000. At p75 (senior at a Tier 1 or Tier 2 employer): $168,000 base plus 13-15% bonus and $25,000-$40,000 annualized equity puts total comp in the $210,000-$230,000 range. At p90 (staff level, Vanguard or Comcast AI platform, or a Susquehanna quantitative role): $198,000+ base with meaningful bonus pushes total cash to $230,000-$260,000, with equity on top for RSU-eligible employers.

Signing bonuses exist in Philadelphia’s MLE market and are recruiter-discretionary. Typical range: $10,000-$25,000 at mid-level, $25,000-$50,000 at senior-to-staff. If you are leaving unvested equity at a current employer, document the unvested amount in dollar terms and ask explicitly for a make-whole payment — this is standard practice at tier-1 tech employers and increasingly common at Comcast, Vanguard, and the larger Philadelphia corporate employers.

Cost-of-living adjusted picture

Philadelphia’s composite cost-of-living index of 104.3 — just 4.3% above the national average — is one of the city’s most underappreciated career advantages. Among cities with a significant MLE job market, Philadelphia is the least expensive major metro after Atlanta, Houston, and Dallas.

Housing is the dominant driver of COL comparisons, and Philadelphia’s market remains accessible by coastal-city standards. Median home prices in the Philadelphia MSA in 2024 ran approximately $320,000-$370,000 depending on zip code. A mid-level MLE earning $138,000 with a standard 20% down payment carries a mortgage of roughly 28-33% of gross income — within conventional lending comfort zones and leaving meaningful room for savings. Compare that to the same calculation in San Francisco ($1.4M median) or even Boston ($750,000-$850,000 median): those markets demand 50-70% of gross income just for homeownership access, before accounting for cost of living on everything else.

Pennsylvania’s flat state income tax rate of 3.07% is among the lowest in the Northeast. On a $138,000 base, that is approximately $4,200 in state income tax. New Jersey residents working in Philadelphia face their state’s graduated rate (5.53-6.37% at this income level), effectively erasing part of the COL advantage if you commute from across the river. California’s graduated rate at this income level hits approximately 8-9%, which costs a California-based MLE an extra $6,700-$8,400 annually versus the Pennsylvania-based peer — partially closing the raw salary gap.

Run the full purchasing-power calculation: Philadelphia’s $138,000 base at COL 104.3, deflated against San Francisco’s 178.6, has roughly $236,000 in equivalent San Francisco purchasing power. The SF p50 for the same mid-level MLE is approximately $185,000-$200,000 — below the Philadelphia-equivalent in real terms. Philadelphia only loses in total economic terms when you reach San Francisco FAANG/AI-lab equity, which is a category of employer that does not have a meaningful Philadelphia presence.

Philadelphia’s COL advantage is most pronounced for housing and taxes; it narrows for dining and entertainment, which run close to national average. Healthcare costs are roughly at the national average. The practical result: an MLE household at p50 in Philadelphia accumulates wealth faster than the identical household at p50 in Boston or NYC, assuming roughly similar risk appetite and spending patterns.

Three-lever negotiation playbook

Philadelphia’s MLE market has structural features that reward specific preparation. These three levers, in order of impact:

1. Remote-eligible offers from national-tier-1 employers anchor the entire conversation. This is the most powerful tool available to a Philadelphia MLE negotiating with a local employer. Major tech employers with remote ML engineering roles — Amazon AWS AI teams, Microsoft Azure ML, Google Cloud, and AI platform companies like Databricks or Snowflake — benchmark compensation at national-tier-1 rates, typically $165,000-$210,000 base for mid-to-senior MLEs. Bringing one of these offers to Comcast, Vanguard, or a regional pharma employer is not a bluff — it is a credible market comparison from a real competitor. Philadelphia’s tier-1 employers have seen remote work erosion of ML talent since 2021 and have become more willing to match or approach national-tier-1 rates for engineers with demonstrated production history. This lever typically yields $15,000-$30,000 above initial band midpoint, an enhanced signing bonus, or both.

2. Level the title before you touch the number. Philadelphia’s major corporate employers — Comcast, Vanguard, Lincoln Financial, Chubb — use internal banding systems where “ML Engineer I,” “Senior ML Engineer,” and “Staff ML Engineer” map to non-overlapping salary bands with meaningful gaps between them. A leveling upgrade from MLE II to Senior MLE at Comcast or Vanguard is worth $20,000-$35,000 in annual base plus a proportionally larger RSU grant and bonus target. The move requires making your case before or during the offer stage — after an offer is extended at a specific level, reversing the level is difficult. Ask the recruiter directly: “Can you confirm the internal level for this role, and walk me through the band range and the equity and bonus guidelines at each level?” Most recruiters at major Philadelphia employers will share this when asked. If your production history — shipped systems, incident ownership, cross-functional design work — supports the senior level, make that case with specifics before the offer call, not after.

3. In pharma and healthcare AI: negotiate the scope, not just the number. When interviewing at GSK, IQVIA, Penn Medicine, or another regulated-industry employer, the most effective negotiation argument is not market data — it is the regulatory complexity premium. MLEs working on FDA-adjacent model validation, clinical trial simulation, or HIPAA-governed data systems are taking on legal and compliance exposure that generalist ML roles do not carry. That specificity has a price. Quantify it: “My prior role involved developing production ML models under 21 CFR Part 11 requirements and presenting to the FDA; that qualification typically commands a 15-20% premium over general MLE roles, and I’m looking for a base that reflects it.” This framing works because it is true and because the hiring manager at a pharma ML team already knows it — they just need you to name it first to give them the internal justification to move the number. This same logic applies to financial MLEs with SR 11-7 model risk management experience or MLEs who have worked on models subject to FCRA fair-lending compliance.

Sign-on bonuses are common for Philadelphia MLE hires in the $10,000-$40,000 range at established employers. If you are starting mid-year and forfeiting an annual bonus payout at your current company, calculate the prorated amount and ask explicitly for a make-whole payment — this is accepted practice at most large corporate employers and almost no candidate asks for it during the offer stage. It is often within recruiter discretion to approve without escalation.

Data caveats

BLS OEWS does not publish a standalone “Machine Learning Engineer” SOC code as of the May 2024 release. The percentile figures on this page are derived from two Philadelphia MSA anchor points in the 2024 OEWS data: the Data Scientists category (SOC 15-2051), where the Philadelphia-Camden-Wilmington MSA p50 was $107,030 and p90 was $174,240, and Software Developers (SOC 15-1252), where the Philadelphia MSA p50 was $130,670 and p90 was $174,980. Machine Learning Engineer roles are distributed across both codes depending on how each employer classifies the position internally. The $138,000 median used here applies a documented MLE specialty premium of 25-30% over the data scientist anchor, consistent with how MLE roles are differentiated from data science roles in the literature and in actual Philadelphia job postings from 2024-2025. ZipRecruiter’s Philadelphia-specific MLE data as of 2025 shows p25 at $102,400 and p75 at $156,400 — broadly consistent with the BLS-adjusted figures above.

Equity is excluded from BLS tracking entirely. For Comcast senior and staff MLEs, annualized RSU grants of $25,000-$50,000 are common; for Susquehanna quantitative roles, equity is minimal but cash compensation substantially higher. For biotech startup equity at pre-IPO Philadelphia companies, realized value is speculative and excluded from the total comp calculations.

The May 2024 OEWS data reflects wages from a survey conducted in 2024. By mid-2026, AI/ML specialization premiums — particularly for LLM engineering and MLOps platform roles — have continued to expand as demand outpaces the supply of engineers with production experience. Treat the figures above as a defensible floor for experienced candidates, not a ceiling for negotiation.

For active benchmarking alongside Philadelphia-specific data, supplement BLS figures with current salary ranges in active job postings (several Philadelphia employers voluntarily post bands even without a state mandate), Levels.fyi reports for Comcast and Vanguard, and your recruiter’s direct statement of the internal band. The triangulation of BLS base, employer-reported bands, and a competing national-tier-1 offer gets you within 8-12% of a defensible number before you walk into the offer conversation. Tracking every active application, compensation detail, and offer timeline in one place removes the cognitive load that makes it easy to undersell yourself under a deadline — OfferFlow’s job board handles that parallel-offer workflow and is free to start.