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About This Role
United States - New York
Investment Banking, Quantitative Analysis, Sales and trading, Trading
Investment Bank
Job Reference #
339164BR
City
New York
Job Type
Full Time
Key responsibilities
We are seeking an experienced candidate to join our eFX Quantitative Analytics Development team, sitting within UBS Global Markets. This is a fast paced and collaborative team that are responsible for the development and enhancement of the best-in-class eFX platform and associated analytics suite. The team sits within the Global Markets Principal Flow Trading stream, and has a business reporting line.
- You will be operating within a high-performing quantitative development team, whose goals are directly aligned to the success of the business
- You will take ownership of initiatives from initial requirement, through, analysis, design, implementation and delivery
- You will proactively suggest and drive through improvements to the platform and our framework
- You will work partner with the eTrading and Quantitative Analyst team to deliver analytics capabilities that will optimising the competitiveness and profitability of the eFX business
- You will understand the financial and technical implications of the decisions and implementation choices that you make
- You will show work across the full analytics stack, showing flexibility by working with the right data and technology stack for a given problem
- You will keep the business at the forefront of technology, by identifying and implementing use cases for machine learning and AI where appropriate
- You will be involved in every aspect of analytics for the eFX business
- Real-time risk and position analytics
- Market share and client optimization tools Backtest and regression tests of different risk and configuration models
- Ad-hoc research, data visualisation, LLM powered analytics, etc
- Regulatory and internal reporting
- You will contribute to shared analytical tools that are utilised by all eTrading businesses within Principal Flow Trading
- You will be willing to work with a global team across different timezones
Your Career Comeback
We are open to applications from career returners. Find out more about our program on ubs.com/careercomeback.
The team
- A highly technical and innovative quant analytics and development team, leading automated trading in FX
- Part of the Principal Flow Trading Quant Development department, alongside teams that are aligned to eRates, eCredit, FX Derivatives, Equity Derivatives, and Cash Equities
- Focused on maximising automation, performance and analytical capabilities in order to drive eTrading revenues
- Operating in a highly agile manner, releasing to production multiple times per day
- The team is known for being collaborative and diverse, with a mandate to deliver meaningful change
- Diversity helps us to grow together. That's why we are committed to fostering and advancing diversity, equity, and inclusion. It strengthens our business and brings value to our client
Your skills and experience
- A strong business knowledge of electronic trading, ideally eFX but any other eTrading business would be beneficial
- A demonstrated understanding of how the business manages risk and generates P&L
- Experience with finance/trading combined with a solid foundation in software development
- Proven experience of designing and implementing analytics solutions for a market making business
- Proven collaboration with sales, trading and quantitative analytics, to design and implement both online and offline analysis in Java/Python, compute-efficient data pipelines along with reliable controls and UI’s to display information
- Experience of leveraging AI to generate analytical tools, queries, scripts and dashboards
- Experience of integrating with and leveraging AI to deliver LLM powered analytical capabilities
- Experience of producing model documentation and partnering with governance and second line of defense functions
Desired technical skills:
- Java (familiarity with low latency Java programming patterns such as lock free data structures and low-garbage programming techniques is beneficial)
- Python 3+
- KDB/Q
- Azure
- Docker / Kubernetes
- Airflow
- React.js
- PowerBI
- Parquet
- DuckDB
- Hands-on experience with LLMs and generative AI concepts(prompts / skills / context engineering / MCP APIs)
- Observability tools (Splunk / Loki / Grafana / Prometheus)
- You’re curious to explore how AI can improve how we build, deliver, and optimize workflows. You do this with sound judgment – validating outputs and aligning with policies, risk standards, and ethical use.
About us
UBS is a leading and truly global wealth manager and the leading universal bank in Switzerland. We also provide diversified asset management solutions and focused investment banking capabilities. Headquartered in Zurich, Switzerland, UBS is present in more than 50 markets around the globe.
We know that great work is never done alone. That’s why we place collaboration at the heart of everything we do. Because together, we’re more than ourselves. Want to find out more? Visit ubs.com/careers.
Salary information
The indicative gross base salary range as a full-time equivalent role:
- United States - New York - New York min USD 225000 - max USD 242500 /annum
The expected salary for this role will be determined by relevant factors which may include but are not limited to, role-required experience, qualifications, education, location and skill level. UBS offers a range of competitive benefits and for further information, please visit ubs.com/employee-benefits. We may, at our sole discretion, provide additional variable compensation or awards. Join us
At UBS, we know that it's our people, with their diverse skills, experiences and backgrounds, who drive our ongoing success. We’re dedicated to our craft and passionate about putting our people first, with new challenges, a supportive team, opportunities to grow and flexible working options when possible. Our inclusive culture brings out the best in our employees, wherever they are on their career journey. And we use artificial intelligence (AI) to work smarter and more efficiently. We also recognize that great work is never done alone. That’s why collaboration is at the heart of everything we do. Because together, we’re more than ourselves.
We’re committed to disability inclusion and if you need reasonable accommodation/adjustments throughout our recruitment process, you can always contact us.
Disclaimer / Policy statements
UBS is an Equal Opportunity Employer. We respect and seek to empower each individual and support the diverse cultures, perspectives, skills and experiences within our workforce.
About BANK-now
BANK-now is a subsidiary of UBS and one of Switzerland’s leading credit and leasing specialists. We have been providing consumer loans for individuals and leasing solutions for individuals and companies since 2007. We are headquartered in the state-of-the-art Panorama building in Horgen with branches across Switzerland and more than 300 employees dedicated to serving our customers. Learn more about us: bank-now.ch/jobs-and-careers
FX - Agentic AI for E-Trader & Algo Workflows | UBS - Experienced professionals - job boards
Salary Context
This $225K-$242K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At UBS, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($233K) sits 10% above the category median. Disclosed range: $225K to $242K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
UBS AI Hiring
UBS has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $242K - $242K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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