Fintech Companies Hiring AI Talent in 2026: Where AI Skills Show Up Beyond ML Engineer Titles

As of 20 October 2026, Finjobsly analysed 4,182 active roles at its 36 tracked employers and found that AI, LLM or machine learning terms appear somewhere in 2,659 of them (64%). That headline is misleading. Once sentences repeated across postings at the same employer are removed, 1,509 roles (36%) still mention AI, and only 423 (10%) name it in the job title or skill tags. Outside engineering and data roles, AI shows up as a tool the candidate is expected to use, for example in marketing, finance and operations, far more often than as a named job. This article measures that spread by function family and employer, and shows where the headline overstates demand.

\n

Key findings

• Finjobsly analysed 4,182 active roles at 36 employers as of 20 October 2026. 2,659 (64%) mention AI terms anywhere in the title, description or skill tags, but for 1,150 of those the only mentions come from sentences repeated at the same employer, such as hiring-process disclosures and company-wide expectations.
• After removing repeated sentences, 1,509 roles (36%) mention AI. Only 423 (10%) name AI or machine learning in the title or skill tags. Outside engineering and data, 874 of 2,802 roles (31%) mention AI in role-specific text, but only 169 (6%) name it in the title or skills.
• Data and machine learning roles are the family where AI is the norm (193 of 287, 67%). Product and design (212 of 407, 52%) is next, ahead of engineering (442 of 1,093, 40%).
• Sales and go-to-market is the opposite case. Only 112 of 922 roles (12%) mention AI after cleaning. Of the 54 roles outside engineering and data with an AI term in the title, 14 are sales roles and 10 of those are Stripe's, where AI mostly describes the customer segment, not the skill.
• Among roles that advertise a USD range, AI-mentioning engineering roles (n=166) have a median advertised range of $188,874 to $246,000, against $192,000 to $240,000 for those that do not (n=216). The gap is small and mixed, and Finjobsly did not control for seniority or employer.

\n

How many fintech jobs actually require AI skills?

It depends on what you count, and the three obvious ways to count give very different answers. All figures are for Finjobsly's tracked panel as of 20 October 2026, 4,182 active roles.

The raw figure is inflated by company-level text. Ten employers with at least 20 roles have an AI term in every active posting: Toast (335 of 335), Brex (261 of 261), Coinbase (225 of 225), Block (217 of 217), Robinhood (153 of 153), Tide (105 of 105), Nubank (99 of 99), Gusto (97 of 97), Carta (74 of 74) and GoCardless (33 of 33). The repeated sentences are of three kinds, and only one is a skill signal. Hiring-process disclosures describe the employer, not the job: Nubank's postings all say its recruitment may use AI-enabled tools such as automated interview transcription and that final decisions are made by human reviewers, and in the 1 October edition of this article Toast, Block, Robinhood and Tide were found to publish similar statements. Product taglines (Brex and Carta describe themselves as AI-native, and GoCardless lists AI-powered fraud tools in the earlier reading) describe the company. Company-wide AI-use expectations are different. The same sentence on using generative AI responsibly with human oversight appears in 189 Coinbase postings, and Gusto states that it expects all team members to engage with AI tools relevant to their role. Those are a genuine hiring signal, but they apply to every role and say nothing about a specific job, so Finjobsly separates them from role-specific mentions.

\n

Which job functions mention AI most?

The table assigns each role to one function family by title (rules in the methodology) and reports the cleaned measure, then the strictest measure. Shares are of the roles in each family. Finance, marketing, people and the catch-all other family are combined because the title rule for those four is the least precise.

Two readings stand out. First, engineering has the largest absolute volume of AI mentions (442 roles) but a lower share than product or data, because engineering is the biggest family and most of it is not AI work. Second, product and the combined finance, marketing, people and other group sit at 39% to 52% on role-specific text and at 10% or less on the strict measure. In those families AI mostly appears as a working method (for example, using generative AI tools in daily workflows), not as a job requirement with its own title.

Stripe is 18.5% of the panel (775 of 4,182), so its postings can move any share. Stripe accounts for 246 of the 1,509 roles that mention AI in role-specific text (16.3%). Excluding Stripe, the share is 37% (1,263 of 3,407), about the same as the panel as a whole, so the overall figure is not a Stripe artefact.

\n

Is the AI mention real or boilerplate? What a hand-read sample showed

The cleaned measure was not hand-checked again for this update. In the 1 October edition, Finjobsly read 20 postings drawn from roles flagged by the cleaned measure whose titles were not engineering, data or AI titles. Fifteen described the person doing the job using AI or AI-related tooling, three described the product, company or partner area, and two were incidental. That sample was small and not statistically representative, and the panel has changed since, but it supports reading the cleaned measure as mostly role-level, with a minority of company description.

The cleaning also has limits. It removes only sentences that appear word for word in 3 or more postings at the same employer, so near-duplicates with a changed ending survive. Coinbase is an example: its generative AI sentence appears in at least five slightly different wordings, so a variant used in fewer than 3 postings would survive the cleaning. The cleaned figure is therefore an upper bound on role-specific AI demand. It also removes legitimate repeated statements, such as a hiring team posting the same AI-tools requirement in 3 near-identical roles, so it may under-count some real requirements.

\n

Which employers mention AI most in their job postings?

The table covers the 34 employers with at least 20 active roles. The raw share shows what a simple keyword search returns. The cleaned share removes repeated sentences. The strict share counts AI or machine learning named in the title or skill tags. Employers are listed by number of roles, which is a count and not a ranking.

The pattern is that the employers with the highest title and skill share are not the largest. GoCardless (10 of 33, 30%), Carta (19 of 74, 26%), Monzo (17 of 68, 25%), Plaid (29 of 132, 22%), FloQast (8 of 38, 21%) and Nubank (20 of 99, 20%) lead on that strict measure, which counts only roles where AI or machine learning is named in the title or skill tags. Gusto stands out on the cleaned measure at 81 of 97 roles (84%), consistent with its company-wide expectation that all staff use AI tools, though much of that expectation is repeated text that the cleaning removed, and what remains is role-specific description. Ramp is the other outlier: 99 of its 169 roles (59%) mention AI in role-specific text, but only 9 (5%) name it in the title or skills. Wealthfront has no AI term in any of its 24 postings. Differences between employers can reflect how a company writes job descriptions as much as how it hires, so use these shares as a description of postings, not of AI maturity.

\n

What do non-engineering AI roles look like?

Few job titles outside engineering and data name AI. Of the 2,802 roles outside those two families, 54 have AI, machine learning, LLM, agentic or generative in the title. Fourteen are sales and go-to-market roles and ten of those are at Stripe, with titles such as "Account Executive, AI Sales", "Technical Account Manager, AI User Segment", "Solutions Architect, AI" and "Strategic Accounts Sales Leader, AI GTM". In most of these roles AI describes the customer or partner segment being sold to, not a skill the employee needs. Block has a Business Development Lead, AI Commercialization, and Ramp has an AI Partnerships Manager. Stripe holds 18 of the 54 in total. The other 40 are spread across product and design (14), other titles (15), marketing (5), finance (2), operations and support (2) and risk, compliance and legal (2). They include a Staff Product Manager, Agentic Commerce at Stripe, a Product Manager, Agentic Commerce at Checkout.com, a Staff Product Designer, AI at Brex, a Senior AI Governance Counsel at Adyen, a Lead Compliance Risk Manager, Product Compliance - AI at Mercury and an AI and Agentic AI Risk Management Senior Specialist at Nubank.

The wider signal is in description text, where AI is a working method layered onto an existing function. Coinbase's repeated sentence about using generative AI tools and copilots in daily workflows, which appears across postings in many functions, is an example. That matches the picture in which fintech jobs are safe from AI. For roles built around supervising AI systems, see the AI agent oversight guide, and for ML and AI engineering employers see fintechs hiring for AI roles.

\n

Do fintech jobs that mention AI pay more?

Not clearly. The table compares USD-paid roles with an advertised range in the same function family, using medians of advertised minimum and maximum, with AI mention defined by the cleaned measure. Ranges with a minimum under $20,000 or a maximum below the minimum were excluded as bad data. A comparison is shown only where both groups have at least 15 roles. Finjobsly did not control for seniority, employer or location, so a gap here is not evidence that AI skills raise pay.

Engineering, the family with the largest sample, shows a mixed picture: the AI group's median minimum is lower and its median maximum higher. Product and design is lower for the AI group on both ends. Data and sales are higher where AI is mentioned, and risk, compliance and legal and finance are lower. Those gaps rest on groups of 19 to 216 roles that differ in seniority and employer mix, so they are descriptive only. Operations, marketing and people are not compared because a group has fewer than 15 roles, and the catch-all other group is not compared. The "AI premium" often cited in commentary is not visible in advertised ranges for engineering and product.

\n

What does external evidence say about AI adoption in financial services?

Finjobsly's data describes job postings, not adoption. For context on firms, the Bank of England and Financial Conduct Authority published a survey in November 2024 of AI use in UK financial services. The Bank of England reported that 75% of surveyed firms (118 responses) were already using AI and that 10% planned to use it over the next three years (Bank of England, Artificial intelligence in UK financial services, published 21 November 2024, accessed 1 October 2026). That survey covers UK regulated firms, not Finjobsly's panel, and does not measure hiring. It is consistent with AI appearing across many functions' job descriptions, but the two datasets cannot be compared directly.

\n

What should candidates outside engineering do with this?

• Read the duties, not the title. In the hand-read sample from the earlier edition, AI appeared in the responsibilities or requirements of roles with no AI in the title.
• Evidence of using AI tools is asked for in non-technical functions. Product roles mention AI in about half of postings and finance, marketing, people and other roles in about two in five, so CVs can show specific workflows where AI was used and what was checked by a person.
• Treat employer-wide statements as culture signals. Employers such as Gusto and Coinbase state a company-wide expectation of AI use, which tells you how the interview may be framed.
• For roles named AI, look at data, engineering and product. Those are the families where AI is in titles and skill tags (124 of 287 data roles, 130 of 1,093 engineering roles, 40 of 407 product and design roles).

\n

Current opportunities

Search live AI and finance roles, browse the fintech jobs page and the company directory to compare employers, or see London roles by function on the London software engineering page.

\n

Methodology

All Finjobsly figures come from the platform's live job database (roles with active status), as of 20 October 2026, covering 36 tracked employers and 4,182 active roles. This panel is not the whole fintech market. AI terms: a case-sensitive whole-word match for AI, or a case-insensitive match for LLM, large language model, generative AI, GenAI, machine learning, agentic, artificial intelligence, prompt engineering and AI agent, applied to the title, the description text (HTML removed) and the skill tags. The strict measure applies the same terms, plus ML as a whole word, to the title and skill tags only. Raw measure: terms anywhere. Cleaned measure: descriptions are split into sentences at full stops, question marks, exclamation marks and line breaks, sentences of 15 characters or fewer are ignored, and any sentence appearing identically in 3 or more postings at the same employer is removed before the terms are applied. This removes hiring-process statements, taglines and repeated company-wide statements, and misses near-duplicate variants, so the cleaned figure is an upper bound on role-specific AI demand. These rules were re-run for this update, and no roles were excluded as placeholders this time, so figures should not be compared with the 1 October edition.

Role families are assigned from job titles using one rule applied across Finjobsly articles, in this order: data and machine learning (data scientist, machine learning, data or analytics engineer, data and business analyst, BI, analytics, and fraud, risk or credit modelling titles), sales and go-to-market (sales, account executive, business development, SDR or BDR, partnerships, customer success, account management, solutions engineering), engineering (engineer, developer, software, SRE, DevOps, architect), product and design, risk, compliance and legal, marketing, finance, people, and operations and support. Titles that match none are counted as other. Title matching is imperfect, so a few roles may sit in a neighbouring family, and finance, marketing, people and other are reported as one group for that reason. In this article, non-engineering and non-data roles means every family other than engineering and data and machine learning. Titles counted as naming AI for the non-engineering section are those with AI, LLM, machine learning, agentic or generative in the title.

The 20-posting sample referred to above was read by an analyst and classified by hand for the 1 October edition and was not repeated. Pay uses USD-paid roles only, medians of advertised minimum and maximum, and excludes minimums under $20,000 or maximums below minimums. Advertised ranges come mainly from employers in jurisdictions with pay-transparency rules, so they are not market averages. Country and city fields are not consistently recorded, so this article reports no location splits. The only external source is the Bank of England.

Last updated: 20 October 2026.

\n

Explore AI-related fintech jobs on Finjobsly

Search AI and finance roles, check pay in the Salary Benchmark, or see how your CV reads against a posting with the CV Analyser and the Career Coach.