Interested in this AI/ML Engineer role at Goldman Sachs?
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Overview
Execute high\-priority and complex projects for the firm by leveraging best\-in\-class project management skills and a strong working knowledge of financial services and operational processes. This role creates an opportunity to lead and deliver key cross\-functional projects, engaging at all stages of the project life cycle.
Located in New York as part of Goldman Sachs' Office of Transformation business unit, you will join a dedicated team responsible for executing strategic projects that advance the firm's priorities and deliver meaningful revenue, risk and control, and efficiency outcomes. Using effective program delivery methods, functional and technical expertise, and stakeholder management skills, this team provides senior management with the execution capabilities to deliver their most complex projects.
This is a Project Manager (Non\-Engineering) role. You will initially focus on an AI\-driven, cross\-divisional global transformation program, contributing to the delivery of a multi\-year firmwide strategic initiative. Through this work, you will develop detailed knowledge of multiple areas across the firm.
Responsibilities
- Project planning and execution: Develop and manage project plans for transformation implementation efforts, including scope, timelines, resources, and deliverables
- Requirements and design: Elicit business requirements and develop target\-state operating models that reflect an AI\-enabled environment
- Delivery: Drive delivery of plans and business cases through active management, working closely with stakeholders to realize impact
- Risk management: Identify risks and issues associated with transformation projects and implement mitigation strategies as needed
- Monitoring and reporting: Track project progress, prepare status reports, and communicate updates to senior management and stakeholders
- Documentation: Maintain comprehensive documentation of project activities, including issue tracking, action plans, and outcomes
- Working groups and steering groups: Organize meetings, prepare materials, and drive active engagement and constructive challenge
- Issue management: Track, escalate, and enable resolution of all actions and issues by their deadlines
- AI\-enabled best practices: Access and share program\-wide tools, dashboards, and other enablers to support teams across the transformation program
Skills and Experience
- 7 to 12 years of experience in project management, consulting, or related roles
- Demonstrated ability to manage multiple projects and meet competing deadlines
- Drive, motivation, and resilience — ability to handle multiple tasks, take initiative, and persist through obstacles
- Ability to develop relationships at all levels across divisions
- Exceptional attention to detail
- Collaborative and team\-oriented mindset; encourages and acknowledges others' contributions
- Strong written and oral communication skills with confidence presenting to senior audiences
- Strong analytical, quantitative, and MS Excel skills
- Excellent critical thinking and problem\-solving abilities
- Ability to coordinate activities with multiple interdependencies across internal teams
- Creativity and innovation — ability to examine current processes and propose improvements
- A strong understanding of financial concepts, products, and services is preferred
This role offers a unique opportunity to contribute to Goldman Sachs' firmwide digital transformation agenda, working on AI\-driven initiatives that span divisions and geographies while building broad institutional knowledge and career growth.
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
© The Goldman Sachs Group, Inc., 2023\. All rights reserved.
Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.
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
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CORPORATE TITLEVice President
language
OFFICE LOCATION(S)New York
assignment
JOB FUNCTIONProject Management
account\_balance
DIVISIONOffice of Regulatory Relations
monetization\_on
SALARY RANGEUSD 100,000 \- 250,000
Salary Context
This $100K-$250K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($175K) sits 20% below the category median. Disclosed range: $100K to $250K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Goldman Sachs AI Hiring
Goldman Sachs has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span New York, NY, US, Richardson, TX, US. Compensation range: $235K - $250K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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