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About This Role
Job Description
WHAT WE DO:
At Goldman Sachs, our Engineers don't just make things – we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action. Create new businesses, transform finance, and explore a world of opportunity at the speed of markets.
Engineering, which is comprised of our Technology Division and global strategists groups, is at the critical center of our business, and our dynamic environment requires innovative strategic thinking and immediate, real solutions. Want to push the limit of digital possibilities? Start here.
APPLIED AI AT GOLDMAN SACHS:
Goldman Sachs Applied AI develops advanced machine learning, statistical modeling, and quantitative research capabilities for complex problems across global financial markets. We combine modern AI methods, rigorous statistical inference, high-dimensional market data, and domain expertise to develop innovative quantitative models and research insights.
Our team operates at the intersection of artificial intelligence, quantitative finance, and market microstructure. We seek to advance the scientific understanding and practical application of machine learning for forecasting, market modeling, execution, liquidity, and systematic trading.
THE ROLE:
Title: Applied AI – Vice President Location: New York, NY Division: Engineering – Applied AI
As a Senior Applied AI Research Engineer, you will conduct original research at the frontier of deep learning, time-series modeling, and quantitative finance. You will formulate important research questions, develop novel predictive and representation-learning methods, and evaluate their statistical and economic relevance using large-scale financial datasets.
You will own research to apply problems end to end: framing the question, curating and engineering the data, designing the model architecture, running large-scale training experiments, building the evaluation harness, and partnering closely with trading desks and engineers to ensure that research is grounded in real market problems and can inform impactful quantitative applications.
WHAT YOU WILL BE WORKING ON:
- Deep Learning, Transformers, and Time-Series Foundation Models: Conduct research on deep-learning methods for financial time-series forecasting, representation learning, and generative modeling. Develop Transformer-based and time-series foundation models using large-scale pre-training, self-supervised learning, transfer learning, and adaptation across assets, market regimes, and forecasting horizons.
- Scientific Evaluation and Applied Quantitative Research: Design rigorous studies of predictive performance, robustness, calibration, and economic relevance using walk-forward testing, leakage controls, regime analysis, and statistical validation. Partner with quantitative researchers and trading stakeholders on research in alpha generation, return and volatility forecasting, liquidity, market microstructure, execution, and portfolio construction.
- Applied quantitative research: Partner with quantitative researchers, strategists, and trading stakeholders to formulate high-value research questions in alpha research, signal generation, return and volatility forecasting, liquidity modeling, market microstructure, execution, and portfolio construction. Translate research results into economically meaningful, risk-aware, and capacity-conscious quantitative insights.
- Research assets and reproducibility: Contribute reusable research artifacts, including pretrained models, datasets, benchmarks, evaluation protocols, and documentation, to support reproducible research and collaboration across asset classes and quantitative research teams.
- Technical leadership and communication: Mentor junior researchers and engineers; provide thoughtful reviews of research design, empirical methodology, and research code; and communicate findings, assumptions, limitations, and recommendations clearly to senior technical and business stakeholders.
- Research governance and responsible AI: Conduct research in accordance with the firm’s model-risk-management, data-governance, privacy, ethics, and responsible-AI standards. Maintain clear documentation of research methodology, data provenance, model assumptions, validation results, and limitations
SKILLS AND EXPERIENCE WE ARE LOOKING FOR:
Required
- Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Electrical Engineering, or a related quantitative discipline.
- Minimum of five years of professional experience in machine learning, quantitative research, applied research, data science, or a closely related field.
- Strong research background in one or more of the following areas: + Deep learning and representation learning + Time-series forecasting and sequential modeling + Quantitative finance, or market microstructure
- Demonstrated ability to formulate and independently execute rigorous research using large, complex, and high-dimensional datasets.
- Ability to communicate complex research ideas, empirical findings, and methodological trade-offs clearly and effectively.
- Ability to work independently on open-ended research questions while collaborating effectively with traders and engineers
Preferred
- Experience with Transformer-based models, probabilistic deep learning, or other modern sequence architectures.
- Experience with multivariate forecasting, cross-sectional prediction, or high-frequency market data.
- Research experience in quantitative trading, systematic investing, market microstructure, electronic execution, market making, statistical arbitrage, or financial econometrics.
- Publications or demonstrated research contributions in leading machine-learning, artificial intelligence, statistics, optimization, or quantitative-finance venues.
- Demonstrated ability to connect methodological advances in AI with robust and economically meaningful results in financial markets.
WHAT'S IN IT FOR YOU:
- Advance frontier research in financial AI: Work on challenging research problems at the intersection of deep learning, Transformer-based modeling, time-series foundation models, and quantitative finance, with the opportunity to generate meaningful commercial impact.
- Access differentiated data and research resources: Apply modern AI methods to proprietary, high-quality cross-asset market data, supported by the computational resources needed for large-scale model training and empirical research.
- Join a rigorous research culture: Be part of a dedicated Applied AI research team that values scientific rigor, strong baselines, robust out-of-sample evaluation, reproducibility, and economically meaningful results.
- Collaborate on real market problems: Partner closely with quantitative researchers, strategists, and trading stakeholders across businesses to formulate impactful research questions in forecasting, alpha generation, liquidity, market microstructure, and execution.
- Engage with the broader research community: Contribute to thought leadership through internal research forums and, subject to applicable firm policies and approvals, external publications and engagement with the AI and quantitative research community.
- Shape the Applied AI research agenda: Develop deep expertise at the intersection of foundation models and financial markets, while helping define the team’s research priorities and future direction
Salary Range
The expected base salary for this New York, New York, United States-based position is $115,000-$30,0000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year-end.
ABOUT GOLDMAN SACHS:
At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.
We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. about our culture, benefits, and people at GS.com/careers.
We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. : https://www.goldmansachs.com/careers/footer/disability-statement.html
We Offer Best-In-Class Benefits Healthcare & Medical Insurance We offer a wide range of health and welfare programs that vary depending on office location. These generally include medical, dental, short-term disability, long-term disability, life, accidental death, labor accident and business travel accident insurance. Holiday & Vacation Policies We offer competitive vacation policies based on employee level and office location. We promote time off from work to recharge by providing generous vacation entitlements and a minimum of three weeks expected vacation usage each year. Financial Wellness & Retirement We assist employees in saving and planning for retirement, offer financial support for higher education, and provide a number of benefits to help employees prepare for the unexpected. We offer live financial education and content on a variety of topics to address the spectrum of employees’ priorities. Health Services We offer a medical advocacy service for employees and family members facing critical health situations, and counseling and referral services through the Employee Assistance Program (EAP). We provide Global Medical, Security and Travel Assistance and a Workplace Ergonomics Program. We also offer state-of-the-art on-site health centers in certain offices. Fitness To encourage employees to live a healthy and active lifestyle, some of our offices feature on-site fitness centers. For eligible employees we typically reimburse fees paid for a fitness club membership or activity (up to a pre-approved amount). Child Care & Family Care We offer on-site child care centers that provide full-time and emergency back-up care, as well as mother and baby rooms and homework rooms. In every office, we provide advice and counseling services, expectant parent resources and transitional programs for parents returning from parental leave. Adoption, surrogacy, egg donation and egg retrieval stipends are also available. Benefits at Goldman Sachs Read more about the full suite of class-leading benefits our firm has to offer.
Opportunity Overview CORPORATE TITLEVice President OFFICE LOCATION(S)New York JOB FUNCTIONQuantitative Engineering DIVISIONEngineering Division SALARY RANGEUSD 130,000 - 260,000
Salary Context
This $115K-$300K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Goldman Sachs, 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 $212,040 based on 13,780 positions with disclosed compensation. Disclosed range: $115K to $300K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Goldman Sachs AI Hiring
Goldman Sachs has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager. Positions span New York, NY, US, New York, NY. Compensation range: $300K - $300K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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