July 20267 min read
AI Salaries in Financial Services: 2026 Compensation Guide
PwC’s 2026 Global AI Jobs Barometer found that financial services roles requiring AI skills carried an average wage premium of 53% in 2025, reaching 78% for AI developer positions.
But AI salaries in financial services vary significantly, even between professionals with similar job titles. A software engineer integrating AI tools, a machine learning engineer deploying models, and a quantitative researcher developing trading signals may all be described as AI talent, but they compete in very different salary markets.
For hiring teams, this means understanding how technical requirements, location, business impact and the full compensation package influence what firms need to offer for each role.
This guide brings together current salary benchmarks for key global financial markets in London, New York, San Francisco and Singapore, explains what drives higher compensation, and shows how firms can benchmark AI roles more accurately.
AI salary benchmarks in finance at a glance
The following benchmarks combine recent Selby Jennings search activity, placements and candidate insights with publicly available compensation disclosures from financial services employers.
| Market | Profile level | Base salary | Total compensation |
| London | Junior AI hire | £50,000 to £60,000 | Base-led package; bonus varies by firm |
| London | Mid-level or applied AI specialist | £110,000 to £160,000+ | Bonus typically additional |
| London | Scarce PhD or front-office AI hire | £200,000 to £250,000 | £270,000 to £320,000 |
| New York | Associate to mid-level AI engineer | $145,000 to $250,000 | Bonus typically additional |
| New York | Senior AI engineer or ML researcher | $185,000 to $300,000 | Bonus typically additional |
| New York | Staff or director-level AI engineer | $220,000 to $400,000 | Bonus typically additional |
| San Francisco | AI engineer supporting investment teams | $150,000 to $200,000 | Discretionary bonus additional |
| San Francisco | AI and data transformation leader | $270,000 to $350,000 | Discretionary bonus additional |
| Singapore | Junior AI researcher or AI quant researcher | SGD 180,000 to SGD 220,000 | SGD 180,000 to SGD 285,000 |
Please note that these are indicative benchmarks only. Bonus, guarantees, equity, deferred compensation and the commercial scope of the role can significantly change the final package.
Static salary data should therefore act as a starting point, supported by current market intelligence - speak to Selby Jennings for AI salary guidance tailored to your role, location and hiring market.
2026 London AI salary benchmarks
London AI compensation varies significantly across banks, hedge funds, asset managers, family offices and proprietary trading firms.
London base salary guide
| AI talent profile | Base salary | Hiring context |
| Junior AI hire, around one year of experience | £50,000 to £60,000 | Similar to a standard graduate technology salary and often too low for stronger candidates |
| Mid-level market data or automation specialist | £110,000 to £120,000 | Relevant for market data, LLM integration and workflow automation |
| Applied AI or machine learning engineer | £120,000 to £150,000 | Common for defined model, engineering or platform responsibilities |
| AI or ML professional with four to six years’ experience | £150,000 to £160,000+ | Seen among stronger banking and established financial services profiles |
| Scarce PhD or front-office AI hire | £200,000 to £250,000 | Common at the upper end of the market, particularly when work connects to research or investment performance |
Upper-end London compensation examples
| Package example | Base salary | Additional compensation | Total compensation |
| PhD-level AI hire | £200,000 to £250,000 | £70,000 guaranteed bonus | £270,000 to £320,000 |
| Applied AI placement | £220,000 | £70,000 guaranteed bonus | £290,000 |
| Technology candidate with markets experience | £100,000 | Around £80,000 equity plus bonus | Around £200,000 |
| Same candidate’s requirement to move | Around £130,000 | Equity, bonus and other incentives | Around £300,000 |
The final two examples in this table show why employers cannot benchmark from base salary alone. A candidate may accept a relatively small base increase but still need a substantially larger overall package to replace unvested equity and bonus payments.
2026 New York AI salary benchmarks
Current base salaries across large investment managers, hedge funds and trading businesses in New York include the following ranges:
| Profile | Base salary |
| Associate-level AI and machine learning engineer | $145,000 to $190,000 |
| NLP or applied AI engineer | $150,000 to $250,000 |
| Generative AI or senior machine learning engineer | $185,000 to $300,000 |
| AI-focused trading or quantitative technology engineer | $220,000 to $300,000 |
| PhD machine learning or quantitative researcher | $235,000 to $300,000 |
| AI and machine learning engineering director | $240,000 to $300,000 |
| Staff machine learning engineer | $300,000 to $400,000 |
These salary ranges are intentionally broad, allowing employers to differentiate compensation based on experience, educational background and technical expertise. A candidate at the bottom and top of the same band may bring very different research, engineering and financial markets backgrounds.
2026 San Francisco AI salary benchmarks
Current financial services hiring activity for AI roles in San Francisco generally falls within the following base salary ranges, with discretionary bonuses often paid in addition.
| Profile | Base salary |
| AI engineer supporting long/short equity teams | $150,000 to $200,000 |
| Applied analytics or forward-deployed AI specialist | $180,000 to $215,000 |
| AI-enabled portfolio engineering leader | $215,000 to $275,000 |
| Head of AI and data transformation | $270,000 to $350,000 |
The higher ranges reflect roles that combine AI knowledge with financial markets expertise, senior stakeholder responsibility or ownership of a wider AI and data strategy.
San Francisco firms also compete directly with the city’s technology companies and AI labs. Financial institutions therefore need to consider the full opportunity, including bonus, equity, technical infrastructure and the scope candidates will have to apply AI to investment and business decisions.
2026 Singapore AI salary benchmarks
Recent Singapore hiring activity indicates the following base salary ranges:
| Employer type | Profile | Base salary |
| Global technology firm | Junior AI researcher | SGD 180,000 |
| Global investment bank | Junior AI quant researcher in global markets | SGD 220,000 |
| Global investment bank | Junior AI researcher in core engineering | SGD 180,000 |
| Global hedge fund | Junior AI quant researcher in an ML trading team | SGD 200,000 |
Total compensation packages are likely to be notably higher than these figures, as Du Qun Yeo, Vice President – Quant & Trading Tech at Selby Jennings APAC, explains:
One junior AI package I worked on had an initial base salary of SGD 180,000, which increased to SGD 285,000 once sign-on, guaranteed bonus and annualized equity were included.
Does every AI role command a salary premium?
No. Simply adding "AI" to a job title does not automatically justify higher compensation.
Zenon Bishop, Director – Financial Technology at Selby Jennings USA, advises that applied AI compensation can remain close to software engineering pay when the responsibilities are similar.
Employers should distinguish between three levels of AI capability:
| Capability | Likely compensation effect |
| Uses AI tools within an existing software, data or research role | Remain within the existing salary band for that function |
| Builds and deploys applied AI systems | Often carries a premium for production, infrastructure and integration experience |
| Develops proprietary models or research linked to investment performance | Can attract the highest salaries, bonuses and guarantees |
What increases AI salaries in financial services?
AI professionals generally command higher compensation when their work can improve trading signals, research coverage, portfolio decisions or client outcomes.
The commercial link does not have to be immediate. For example, an engineer building a platform used by several investment teams may still command a high package because of the scale of the impact.
Many candidates can build a proof of concept, but managing data quality, testing, deployment, monitoring, security and performance at scale are skills far fewer have built.
This makes experienced applied AI engineers, MLOps specialists and machine learning infrastructure engineers harder to find, and in turn more expensive to secure and retain.
Relevant PhDs, published research and experience with natural language processing, deep learning or foundational models can increase compensation.
The premium is by far the strongest when candidates can apply that research outside academia and have experience building tools that businesses can use.
Market knowledge becomes more valuable as the role moves closer to portfolio managers, quantitative researchers or trading desks.
An engineer building shared infrastructure may not need detailed asset-class experience, but a researcher working on credit, equities, macro or commodity signals usually will.
A candidate hired to set the AI architecture, establish a new team or define firm-wide technical standards will expect more than someone joining an established function.
The scope behind titles such as “Head of AI” or “Director of Machine Learning” should therefore form part of the benchmark.
What should a compensation package include for AI talent?
In addition to base salary, financial institutions may need to include the following elements as part of a competitive compensation package:
- Annual bonus
- Guaranteed bonus
- Sign-on payment
- Equity or restricted stock
- Deferred compensation
- Buyouts for forfeited awards
- Fund-linked incentives
Joshka Van Der Walt, Principal Consultant – Quants at Selby Jennings London, explains that equity gives technology employers a strong advantage over other industries when competing for machine learning talent.
Financial services firms can compete by emphasizing access to unique datasets, commercially impactful work, and a direct link between the tools a candidate builds and the outcomes they influence:
Within finance, much of the work is applied. You build something that needs to work, and you receive direct feedback through making money or not making money.
How should finance firms benchmark AI compensation?
Start by defining the work involved rather than relying solely on a job title.
Answering the following five questions will enable more accurate compensation benchmarking:
1. What will the hire build or manage?
Distinguish original research, model development, production engineering, infrastructure and third-party tool integration.
2. Which employers are competing for the same talent?
Compare the role with similar functions at banks, hedge funds, trading firms, asset managers or technology employers.
3. How close is the work to revenue?
Research and engineering tied to trading or investment performance may sit above internal efficiency roles.
4. What is the full candidate package?
Include bonus, equity, deferred awards and any payments the candidate would lose by moving.
5. How many realistic candidates meet the complete brief?
The smaller the qualified talent pool, the more pressure there will be on salary, bonus and the wider package.
Benchmark and hire AI talent with confidence
Selby Jennings has specialist teams working across the functions shaping AI adoption in financial sciences and services, from machine learning and data engineering to quantitative research, trading and investment management.
Our cross-market expertise allows us to advise firms on the profiles they need, what those candidates expect and how compensation differs by role, location and commercial impact.
Hiring AI talent? Request a call back for more information, or tailored AI salary benchmarking, talent mapping and recruitment support.
Considering your next move? Register with Selby Jennings or browse exclusive AI and machine learning roles around the globe.
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