Machine Learning Engineer Salary in Chicago — 2026 BLS Data
Salary distribution
Percentile breakdown of Machine Learning Engineer base salaries in Chicago.
The BLS OEWS May 2024 data for the Chicago-Naperville-Elgin MSA puts the median base salary for workers in SOC 15-2051 — the government’s closest classification to Machine Learning Engineer — at $112,640. That number is essentially flat against the national median of $112,590 for the same occupation code, a near-tie that masks a market with very different internal dynamics than the national average suggests. Chicago’s ML labor market is shaped by one of the country’s most concentrated clusters of quantitative trading firms, a massive financial services corridor, and a handful of major tech companies — all competing for a relatively thin supply of engineers who can build and ship production ML systems. The median doesn’t tell you much. The distribution does.
Chicago machine learning engineer salary percentiles (BLS OEWS May 2024)
The Chicago-Naperville-Elgin MSA percentile breakdown from BLS OEWS May 2024 (SOC 15-2051, Data Scientists — the federal classification used as the proxy for machine learning engineering roles), sourced via O*NET’s regional wage tool:
| Percentile | Annual Base Salary |
|---|---|
| 25th (P25) | $82,150 |
| 50th (P50, median) | $112,640 |
| 75th (P75) | $147,130 |
| 90th (P90) | $175,390 |
The P25-to-P90 spread of $93,240 inside a single metro area is telling. Nationally that same occupation shows a P25 of $82,630 and a P90 of $194,410 — a spread of $111,780. Chicago’s narrower spread at the top reflects two things simultaneously: a floor that is roughly at the national average (the city has a high cost of living that pushes entry-level pay up), and a ceiling that comes in somewhat below the tech-dominated metros like San Francisco or Seattle, where FAANG equity packages stretch the P90 considerably higher. Chicago’s P90 at $175,390 is a solid number — but it describes a market where the high end is driven by finance and quant firms paying cash-heavy packages, not by startup equity or mega-cap stock grants.
What the median hides
The $112,640 median merges a genuinely wide range of roles under one label. BLS OEWS aggregates every worker coded to this SOC group across all industries and seniority levels in the metro area. A few dynamics the number obscures:
The financial sector bimodality. Chicago’s ML job market has a structural split that most cities don’t. On one side sit the quantitative trading firms — Citadel, IMC Trading, Jump Trading, Chicago Trading Company, DRW — where ML engineers work on ultra-low-latency prediction, signal research, and execution systems. These roles pay dramatically above the median: IMC Trading’s posted base range for ML Engineer roles runs $175,000–$250,000; Citadel software engineers (who overlap substantially with ML roles) show a median total compensation package of $588,000 on Levels.fyi. On the other side sit the large corporate employers — United Airlines, McDonald’s, Walgreens Boots Alliance, Abbott, Morningstar, and dozens of mid-market tech companies — where ML engineering roles are more conventional and pay in the $110,000–$160,000 range. The BLS median lands squarely in the corporate tier and tells you almost nothing about what the quant and finance firms pay.
Level compression in the corporate sector. The P25 of $82,150 captures two distinct groups: early-career ML engineers at large corporate employers who haven’t yet differentiated between data analysts and production-ML specialists, and mid-career practitioners at smaller companies or healthcare systems with less mature data organizations. An ML engineer with two years of production deployment experience — models in serving infrastructure, not just notebooks — should not be reading the P25 as a relevant number. It is a floor, not a benchmark.
Remote work participation in the Chicago sample. A portion of the Chicago-area workers counted in the OEWS data are employed by remote-first companies headquartered elsewhere. This effect modestly inflates the P75 and P90 relative to what a traditional Chicago-headquartered employer would offer, because coastal tech companies paying coastal rates populate the upper tail even while their employee lives on the North Side.
SOC 15-2051 is the closest available BLS code, not a perfect match. The Bureau of Labor Statistics does not have a dedicated “Machine Learning Engineer” occupational code. SOC 15-2051 (Data Scientists) is the code that most closely aligns with ML engineering roles and is the source for these figures. Some ML engineers who hold titles like “Software Engineer, ML” may be coded to SOC 15-1252 (Software Developers) instead, which had a national median of $133,080 in May 2024 — meaningfully higher. The true market distribution for people with ML engineering skills likely sits somewhat above the figures in the SOC 15-2051 table, particularly at the P75 and P90.
How Chicago compares to other major ML hubs
Chicago is not a top-tier ML compensation market on a gross salary basis, but it is materially better than its COL-adjusted rank suggests once you run the real numbers.
San Francisco Bay Area anchors the national ceiling. Bay Area ML engineers at major tech companies or top-tier AI startups routinely see base salaries of $180,000–$250,000 with total compensation (including RSUs) pushing well past $350,000 for mid-level roles. The national P90 of $194,410 for this occupation code understates the Bay Area picture because the highest earners are increasingly in tech-specific codes. But the Bay Area’s COL index runs around 178 — housing costs alone in San Francisco proper average over $1.1 million for a single-family home. The real purchasing power advantage over Chicago is much narrower than the gross salary gap.
Seattle (COL index ~135) pays ML engineers a median base in the $150,000–$170,000 range at the major employers (Amazon, Microsoft, Meta’s Seattle office). Equity packages are more generous than Chicago’s corporate average. Seattle’s edge over Chicago on gross pay is real — roughly $30,000–$40,000 at the median — but the higher COL and Washington State’s graduated income tax situation (no state income tax, but high property and sales taxes) erodes some of that.
New York City (COL index ~187) presents a similar dynamic to Chicago in one important way: financial services is a major ML employer. Goldman Sachs, Two Sigma, Renaissance Technologies, and JPMorgan all run substantial ML teams in NYC, and they pay at levels comparable to Chicago’s quant trading firms. The difference is that NYC also has a large FAANG presence that Chicago lacks, which pushes the gross-pay ceiling higher. But NYC’s combined state and city income tax (up to 12.7% marginal) significantly reduces take-home relative to Illinois’s flat 4.95% state rate.
Austin (COL index ~119) has grown rapidly as a tech hub but its ML market is less deep than Chicago’s. The median ML salary in Austin runs approximately $125,000–$135,000 base, driven by Tesla, Oracle, Apple, and a growing venture-backed startup ecosystem. Austin has no state income tax, which helps the take-home comparison, and its COL advantage over Chicago is real on housing. But it lacks Chicago’s financial-sector ML depth.
The honest comparison: a P75 offer in Chicago ($147,000 base, Illinois flat tax at 4.95%) compares favorably to a median offer in Seattle or NYC on a net-income and purchasing-power basis once COL and taxes are run through properly.
What drives the spread: company tier, level, and specialty
Three variables explain roughly 80% of the $93,000 gap between P25 and P90 in the Chicago ML market.
Company tier
Chicago’s ML employer landscape breaks into three clear tiers by pay:
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Tier 1 — Quantitative finance ($175,000–$300,000+ base): Citadel, IMC Trading, Jump Trading, DRW, Belvedere Trading, Chicago Trading Company, Optiver. These firms hire a small number of ML engineers with extremely deep technical requirements (strong computer science fundamentals, distributed systems, optimization theory, often some background in statistics or signal processing). Base salaries here start above the Chicago P90, and cash bonuses can equal or exceed base. Equity is less common than at tech companies, but total cash compensation for a mid-level ML hire at one of these firms routinely reaches $350,000–$600,000 including performance bonus. JPMorgan Chase ML Engineer roles at the mid-level show a median total comp of $190,000 on Levels.fyi — the lower end of the financial-sector spectrum.
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Tier 2 — Large corporate tech and tech-adjacent ($120,000–$175,000 base): Google (2,000+ employees in Chicago, expanding cloud and AI infrastructure), Microsoft, United Airlines (large ML team for operations and pricing), McDonald’s (global tech organization with substantial ML investment), Morningstar, Avant, Relativity. These employers pay solid market rates, offer RSUs or stock purchase plans, and typically have more structured level progressions. A senior ML engineer at a Tier 2 employer targets $145,000–$165,000 base plus 10–15% bonus plus $30,000–$60,000 in annual equity value.
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Tier 3 — Mid-market and non-tech ($85,000–$120,000 base): Healthcare systems (Northwestern Medicine, Rush, Advocate Health), regional financial institutions, insurance carriers, logistics companies, and mid-market SaaS. These employers often list roles as “data scientist” or “ML engineer” interchangeably and may not have production ML infrastructure. They populate the lower two quartiles of the OEWS distribution heavily.
Moving from Tier 3 to Tier 2 at the same experience level typically adds $25,000–$40,000 in base salary. Moving from Tier 2 to Tier 1 requires meeting a significantly higher technical bar but can add $80,000–$150,000 in total cash.
Level and experience
Chicago’s corporate employers tend to use structured ML engineering levels, roughly:
- MLE I / Junior (0–2 YOE): $85,000–$105,000
- MLE II / Mid-level (2–4 YOE): $105,000–$135,000
- Senior MLE (4–7 YOE): $135,000–$165,000
- Staff / Principal MLE (7+ YOE, technical lead scope): $165,000–$210,000+
The jump from mid-level to senior is the single highest-leverage career move at most Chicago employers. It often requires concrete evidence of production ownership: a model you deployed and maintained, an improvement to inference latency you shipped, a retraining pipeline you built end-to-end. Without that evidence, the title change may happen but the pay step-up is smaller.
Specialty premium
Not all ML specializations pay equally in the Chicago market. The city’s industry mix creates specific demand pockets:
MLOps and production ML infrastructure: Consistently commands a premium of $15,000–$25,000 above pure modeling roles. Chicago’s large enterprise employers have significant backlogs of models that were built but never properly productionized, and engineers who can build reliable serving infrastructure are scarce.
NLP and LLM fine-tuning: High demand at financial services firms (contract analysis, earnings call processing, risk documentation) and healthcare systems (clinical notes, prior authorization). Premium runs 15–25% above the generalist median in 2025–2026.
Quantitative ML / signal research: The most Chicago-specific premium. Engineers who combine ML skills with quantitative finance intuition — understanding market microstructure, working with tick data, reasoning about overfitting in non-stationary financial time series — are extremely scarce. This is the specialty that unlocks the Tier 1 trading firm pay scale.
Computer vision: Less embedded in Chicago’s core industries than NLP or tabular/time-series ML. Boeing has some aerospace applications; a few healthcare and industrial firms use vision systems. The market is smaller and premium over generalist ML is modest.
Ask in your first screening call: “What does your model deployment process look like — how do models get from development to production?” If the answer is vague or the team is still “building the infrastructure,” you are almost certainly in Tier 3.
Total compensation: beyond the base salary
BLS OEWS tracks wages only — bonuses, equity, and benefits are excluded entirely from the published figures. For ML engineers, the gap between base and total compensation is significant.
A representative mid-level ML engineer at a Chicago Tier 2 employer (large tech company or enterprise with mature ML organization) might see a package structured roughly as follows:
- Base salary: $112,640 — the BLS median for this occupation in Chicago. This is the number on your W-2 and what your retirement contributions are calculated against.
- Annual performance bonus: ~$17,000 — typical corporate bonus pools for ML engineers run 10–18% of base at large employers. Financial services skews higher (15–25% is common); enterprise tech and healthcare skew lower (8–12%).
- Annual equity value: ~$18,000 — corporate Chicago employers typically grant RSUs vesting over three to four years. A mid-level hire at a large tech company might receive an initial grant of $60,000–$80,000 total, yielding approximately $15,000–$20,000 per year. This is significantly lower than Bay Area tech RSU grants at comparable roles, but it is real and worth tracking in negotiation.
That produces a representative total compensation of roughly $148,000 for a median-earning ML engineer in Chicago. Senior roles ($145,000–$165,000 base) with 15% bonus and more substantial equity grants routinely reach $195,000–$225,000 total comp at Tier 2 employers.
At Tier 1 quant firms, the math is different in every dimension. Cash bonuses at firms like Citadel are not percentage-of-base — they are discretionary pools tied to firm performance, and for strong performers they can equal or exceed annual base salary. An ML engineer at a top Chicago trading firm earning $200,000 base might receive a total package of $400,000–$700,000 in a strong year. The variance is high, the technical bar is extreme, and the environment is different from corporate tech in most observable ways — but the compensation is in a different class.
Illinois charges a flat 4.95% state income tax on all income. On a $112,640 base, that is $5,575 — lower than California’s graduated rate that would apply $6,000–$11,000 at the same income level, and lower than New York’s combined state/city rate of up to 12.7%. This difference is smaller than Texas’s zero-tax advantage, but it is not nothing.
Cost-of-living adjusted picture
Chicago’s COL index sits at approximately 107 — about 7% above the US national average. That is a mild premium, well below coastal tech hubs, driven primarily by housing costs that run roughly 30–40% above the national average. The median single-family home in the Chicago MSA was around $330,000–$350,000 in early 2026; in the city proper and desirable North Side neighborhoods prices run higher, but suburban options in the metro area (Evanston, Oak Park, Naperville) offer more comparable pricing to Sun Belt metros. A one-bedroom in the city runs $1,700–$2,100 per month in most neighborhoods; further out or in emerging neighborhoods the range drops to $1,400–$1,700.
Working through the COL adjustment: a $112,640 Chicago base has the purchasing power of approximately $105,300 at the US national average, or a $177,000 salary in San Francisco (COL ~178), or $130,000 in Denver (COL ~116). The comparison to San Francisco is the most striking — an ML engineer accepting $112,640 in Chicago has roughly equivalent real purchasing power to a colleague earning $177,000 in SF. That advantage holds across most consumer categories except local services, where San Francisco and New York tend to run higher.
Chicago also has no city income tax beyond the state flat rate, which distinguishes it from NYC (where city tax adds another 3.9% at moderate incomes) and is a meaningful quality-of-life benefit in salary planning.
Three-lever negotiation playbook
Chicago has specific negotiation dynamics that differ from coastal tech markets. Equity is rarely the primary battleground — cash compensation (base and bonus) is where most leverage lives.
Lever 1: Anchor to P75 and reference the finance market
The BLS P75 for this occupation in Chicago is $147,130. That is the appropriate starting anchor for any ML engineer with three or more years of experience who can demonstrate production ML work. More importantly, Chicago is unusual in having a visible finance-sector benchmark: you can reference, specifically, that ML engineers at Chicago-based financial institutions command substantially higher base salaries for comparable ML work. Even if you are not targeting quant firms, the existence of that market exerts upward pressure on your offer, and recruiters at corporate employers know it. A concrete anchor works: “Based on BLS OEWS data for Chicago and market rates I’ve researched for this role in the city, I’m targeting a base in the $145,000–$150,000 range.” Specificity signals you’ve done the homework.
Lever 2: Target the bonus target percentage in writing
In Chicago’s large corporate environment — financial services, big tech, enterprise — bonus target percentages are often more negotiable than base salary bands, which are managed rigidly by HR compensation systems. A move from a 10% target bonus to a 15% target bonus on a $130,000 base is $6,500 per year, and it often encounters less HR friction because it is framed as performance-contingent. Get the bonus target percentage in the offer letter, not just mentioned verbally. Ask explicitly: “Is there flexibility to move the bonus target to 15%? For senior ML roles in the Chicago market, that’s fairly standard at financial services and large tech employers.” The answer will tell you how constrained the company’s comp system really is.
Lever 3: Negotiate the total RSU grant, not the annualized figure
Most Chicago corporate employers quote equity as “$X per year” in the offer letter, which obscures the actual negotiation. Ask for the total grant amount and the vesting schedule. A $72,000 grant vesting over four years is a more concrete number to negotiate against than “$18,000/year in RSUs.” Initial grants are almost always more negotiable at offer time than in annual review cycles, because at hire the recruiter has budget flexibility that disappears once you’re inside the system. Pushing a $60,000 total grant to $80,000 at the offer stage — $20,000 in additional equity over four years — is a common successful ask and rarely results in an offer being withdrawn. Frame it directly: “On the equity side, is there room to bring the initial grant up to $80,000 total? I want to find a structure that works for both of us without having to move on base.”
A strong competing offer, disclosed specifically (“I have an offer at $145,000 base from [company]”), is the most effective negotiation tool in any market. Chicago recruiters, particularly at financial services firms and large tech companies, can and will escalate to comp review if you give them a concrete number to defend against. Do not leave it vague.
Data caveats
BLS OEWS is the most reliable public-domain wage source available, but several limitations apply specifically to this occupation code and this city:
The SOC code is imprecise. BLS does not have a standalone “Machine Learning Engineer” code. SOC 15-2051 (Data Scientists) captures most ML engineering roles but misses some that are coded to 15-1252 (Software Developers). The national median for Software Developers was $133,080 in May 2024 — $20,000 above the 15-2051 median. If you hold a title like “Software Engineer, Machine Learning” or “ML Infrastructure Engineer,” your comp should be triangulated against both SOC codes.
Equity is excluded entirely. OEWS tracks wages only. At Tier 1 quant firms where annual cash bonuses can equal base, and at senior levels at tech companies where RSU grants represent 20–30% of total comp, the percentile tables above describe a fraction of what top earners actually take home.
The data is lagged. May 2024 reflects wages paid in 2024. Demand for LLM and generative AI engineering skills has continued to outpace supply through 2025–2026, and the upper percentiles for ML engineers with those specific skills are meaningfully higher in the current market than the 2024 survey captures. Add approximately 8–12% to P75 and P90 figures if you have demonstrable experience with production LLM deployment, fine-tuning, or retrieval-augmented generation systems.
Chicago’s finance-sector premium is not in the median. The quant trading firms represent a small share of ML engineering employment by headcount, which means their outsized pay has limited effect on the P50 and P25. The median reflects the much larger corporate employer base. If you are targeting or have an offer from a quant firm, the OEWS percentiles above are not the reference point — look at firm-specific data on Levels.fyi and direct comparisons to similar roles.
The MSA is large. Chicago-Naperville-Elgin spans multiple counties in Illinois, Indiana, and Wisconsin. A role at Motorola Solutions’ Schaumburg campus, a quant firm in the Loop, a pharma company in Lake County, and a tech startup in the West Loop are all inside the same OEWS data pool — and they pay quite differently.
For a complete picture, triangulate BLS OEWS against: O*NET’s local wage tool (which directly surfaces the same BLS data in a cleaner interface), Levels.fyi for named-employer data at finance and tech firms, and actual posted salary ranges in Chicago job listings. Illinois does not have a salary range disclosure law for all employers, but large employers increasingly include ranges voluntarily. Between those sources, you can estimate within 8–10% of what any specific offer should look like before the negotiation starts.
Tracking multiple Chicago applications simultaneously — monitoring where each process stands, comparing compensation structures, keeping notes on what each company has shared about their ML stack — is where a dedicated job tracker pays off. Salary negotiation is easier when you can see competing offers side by side.