Finance AI jobs in 2026: JPMorgan has 9 VP-level AI roles open. OpenAI has zero.
OpenAI has 101 open AI/ML roles in HireIndex’s September 7, 2026 snapshot — more than any other company in the index. Exactly zero of them are VP-level, director-level, or executive director-level. JPMorgan, by contrast, has 31 AI roles open across its two entity listings, and 9 of them carry VP, Executive Director, or senior applied AI lead titles at that level.
That gap is not a fluke. It reflects something structural about how banks are building AI teams versus how labs are building them.
Which financial institutions are hiring for AI right now?
Across 11 financial institutions — banks, investment banks, and fintechs — HireIndex’s September 7 snapshot shows 171 open AI and machine learning roles spanning five countries.
| Institution | Open roles | Leadership roles | Remote |
|---|---|---|---|
| Capital One | 65 | 3 | 54% |
| JPMorgan | 31 | 9 | 52% |
| BMO | 22 | 6 | 9% |
| TD | 15 | 0 | 73% |
| Plaid | 12 | 0 | 0% |
| Westpac | 7 | 0 | 29% |
| Royal Bank of Canada | 7 | 1 | 100% |
| Commonwealth Bank | 6 | 0 | 67% |
| Citi | 2 | 2 | 0% |
| Morgan Stanley | 2 | 0 | 50% |
| Barclays | 2 | 0 | 100% |
Capital One (65) is the third-largest AI employer in the entire HireIndex index, behind only OpenAI (101) and Databricks (67). It has more open AI roles than Scale AI (56), Amazon (45), or Reddit (36). Combined, these 11 institutions account for 8.4% of the 2,040 AI/ML roles tracked in this snapshot.
The BMO row combines BMO Financial and BMO’s separate entity listings in the source data. JPMorgan combines JPMorganChase and J.P. Morgan listings.
Why does finance AI hiring skew so senior?
Across all 2,040 roles in the September 7 snapshot, 4.1% are classified as leadership-level (VP, director, or above). For the 171 finance AI roles, that figure is 12.3% — three times higher.
JPMorgan’s 9 leadership roles include “Executive Director, Machine Learning & Gen AI Platforms,” “Applied AI ML Lead Engineer, VP — Asset and Wealth Management,” and “AI Algorithms Research Scientist — Vice President.” BMO’s 6 include a Director of AI Architecture B2C and two VP of Data Science positions.
These are not recently promoted team leads. They’re senior hires brought in to build AI functions inside institutions with existing compliance frameworks, approval chains, and multi-decade technology stacks. A bank launching an LLM inference platform for markets operations needs someone who can navigate internal governance, not someone who needs to learn what governance is.
Labs hire differently because they’ve had AI teams for years. OpenAI doesn’t need to hire a VP of Machine Learning — those people are already there. It needs machine learning engineers and applied AI engineers who can ship products fast. That’s why 97 of OpenAI’s 101 open roles are mid-level.
Banks are at an earlier stage of institutional AI capability, and the job market reflects it.
Are finance AI jobs remote?
Finance AI roles are less remote than the broader market. Across all 2,040 jobs in the index, 61.8% list as remote. For finance AI, that drops to 46.8%.
The distribution varies sharply by institution. Royal Bank of Canada’s 7 roles are all remote. Barclays’ 2 are remote. Plaid’s 12 San Francisco roles are entirely in-person. BMO’s Canadian roles are almost entirely office-based — 20 of 22 require showing up in Toronto. Westpac’s Sydney roles are office-heavy.
JPMorgan splits across four cities: London holds 10 roles, New York 4, San Francisco 1, with 16 listed remote. It’s the most geographically distributed institution in this group.
For data scientist candidates who need full schedule and location flexibility, the Canadian banks narrow the field significantly. BMO and TD’s in-office-heavy postings are functionally Toronto-only.
What technology are banks actually hiring for?
Capital One’s open roles span LLM inference, Agentic AI, Vision Language Models, Kubeflow, and Gen AI evaluation — a full AI platform build, not a few scattered ML experiments. JPMorgan’s tech signals include LLM engineering, agentic AI, and ML platform infrastructure, deployed across markets operations, home lending, and asset management. BMO’s postings flag generative AI and AI-enabled systems as the primary drivers. Westpac, notably, has established a dedicated AI Innovation Office and is staffing it with engineers, quantitative analysts, and data scientists simultaneously.
The technology stack converges on the same tools as the labs — Python, AWS, LLM frameworks. The use cases are entirely different: financial crimes detection, credit underwriting, compliance automation, customer-facing recommendation systems. A machine learning engineer moving from a lab to a bank will recognize the tooling and spend the first few months learning what a risk committee is.
Finance AI vs the largest AI labs
| Finance sector | OpenAI + Databricks + Scale AI | |
|---|---|---|
| Open roles | 171 | 224 |
| Leadership rate | 12.3% | 3.6% |
| Remote rate | 46.8% | 59.4% |
| Dominant locations | NY, Toronto, London, SF | SF, fully remote |
| Most common seniority | Senior | Mid |
The 11 financial institutions have nearly as many AI roles as the three largest AI labs combined, with a leadership mix that’s more than three times higher.
JPMorgan’s 9 VP-level and above AI openings alone outnumber the combined leadership openings at OpenAI (0), Scale AI (3), and Databricks (5) — which together total 8.