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About This Role
VP \- AI/ML Engineer \- Compliance Engineering
YOUR IMPACT
Are you passionate about leveraging cutting\-edge AI/ML techniques, including Large Language Models, to solve complex, mission\-critical problems in a dynamic environment? Do you want to contribute to safeguarding a leading global financial institution?
OUR IMPACT
We are Compliance Engineering, a global team of engineers and scientists dedicated to preventing, detecting, and mitigating regulatory and reputational risks across Goldman Sachs. We build and operate a suite of platforms and applications that protect the firm and its clients.
We offer:
- Access to petabyte scale of structured and unstructured data to fuel your AI/ML models, including textual data suitable for LLM applications.
- The opportunity to work with state\-of\-the\-art LLM models and agentic framework.
- A collaborative environment where you can learn from and contribute to a team of experienced engineers and scientists.
- The chance to make a tangible impact on the firm's ability to manage risk and maintain its reputation.
Within Compliance Engineering, we are seeking an experienced AI/ML Engineer to join our Engineering team. This role will focus on solving highly complex business problems using AI/ML techniques, incorporating latest emerging trends om building out vertical AI agents to run on data at massive scale.
HOW YOU WILL FULFILL YOUR POTENTIAL
As a member of our team, you will:
- Design and architect scalable and reliable end\-to\-end AI/ML solutions specifically tailored for compliance applications, ensuring adherence to relevant regulatory requirements. This encompasses the development and implementation of GenAI\-driven solutions, including agentic frameworks for automating compliance processes, RAG pipelines, and the creation and utilization of embeddings for compliance knowledge bases.
- Explore diverse AI/ML problems, such as model fine\-tuning, prompt engineering, and experimentation with different algorithmic approaches to address novel business challenges.
- Develop, test, and maintain high\-quality, production\-ready code.
- Lead technical projects from inception to completion, providing guidance and mentorship to junior engineers.
- Collaborate effectively with compliance officers, legal counsel, and other stakeholders to understand business requirements and translate them into technical solutions.
- Participate in code reviews to ensure code quality, maintainability, and adherence to coding standards. Promote best practices for AI/ML development, including version control, testing, and documentation.
- Stay current with the latest advancements in AI/ML platforms, tools, and techniques to solve business problems.
QUALIFICATIONS
A successful candidate will possess the following attributes:
- A Bachelor's, Master's or PhD degree in Computer Science, Machine Learning, Mathematics, or a similar field of study.
- Preferably 7\+ years AI/ML industry experience for Bachelor’s/Masters, 4\+ years for PhD with a focus on Language Models.
- Strong foundation in machine learning algorithms, including deep learning architectures (e.g., transformers, RNNs, CNNs)
- Proficiency in Python and relevant libraries/frameworks such as TensorFlow, PyTorch, Hugging Face Transformers, scikit\-learn.
- Demonstrated expertise in GenAI techniques, including but not limited to Retrieval\-Augmented Generation (RAG), model fine\-tuning, prompt engineering, AI agents, and evaluation techniques.
- Experience working with embedding models and vector databases.
- Experience with MLOps practices, including model deployment, containerization (Docker, kubernetes), CI/CD, and model monitoring.
- Strong verbal and written communication skills.
- Curiosity, ownership and willingness to work in a collaborative environment.
- Proven ability to mentor and guide junior engineers.
Experience in some of the following is desired and can set you apart from other candidates:
- Experience with Agentic Frameworks (e.g., Langchain, AutoGen) and their application to real\-world problems.
- Understanding of scalability and performance optimization techniques for real\-time inference such as quantization, pruning, and knowledge distillation.
- Experience with model interpretability techniques.
- Prior experience in code reviews/ architecture design for distributed systems.
- Experience with data governance and data quality principles.
- Familiarity with financial regulations and compliance requirements.
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 TITLE
Vice President
OFFICE LOCATION(S)
New York
JOB FUNCTION
Software Engineering
DIVISION
Compliance Division
SALARY RANGE
USD 130,000 \- 250,000
Salary Context
This $130K-$250K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. This role's midpoint ($190K) sits 6% above the category median. Disclosed range: $130K to $250K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Goldman Sachs AI Hiring
Goldman Sachs has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Richardson, TX, US, Dallas, TX, US. Compensation range: $160K - $250K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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