Interested in this AI/ML Engineer role at Goldman Sachs?
Apply Now →Skills & Technologies
About This Role
Summary
The AI Innovation and Solutions (AIS) team operates with the speed and spirit of a startup, focused on rapidly prototyping and building production\-grade, cloud\-native AI applications that integrate cutting\-edge AI capabilities to directly address the critical needs of our businesses. Our primary goal is to demonstrate the transformative potential of AI within the firm through accelerated application delivery, rapidly deploying impactful solutions, and then seamlessly transferring the application code, cloud integration patterns, robust data models, and operational knowledge to respective business and engineering teams. This hands\-on engineering role is pivotal in shaping the future of AI adoption at Goldman Sachs by building reliable, highly scalable, cloud\-optimized AI\-powered products and fostering a culture of innovation and rapid, continuous delivery.
As an AI Application Engineer, you will be instrumental in designing, building, and deploying end\-to\-end, cloud\-native AI applications that leverage advanced AI/Machine Learning solutions to drive tangible business value. You will thrive in a fast\-paced environment, leveraging your expertise to translate complex business challenges and customer needs into actionable cloud\-based application architectures, optimized data models, and technical specifications that incorporate AI capabilities, and then implement and deliver these systems with a focus on speed, reliability, and operational excellence.
Key Responsibilities
- Rapid Prototyping \& Application Development: Lead the end\-to\-end development of applications that integrate and leverage AI/ML models, from architectural design, data schema design, data pipeline construction, and rapid prototyping to initial deployment and operationalization, utilizing cloud\-native services (e.g., serverless, containerization, managed AI/ML platforms) and CI/CD pipelines for accelerated delivery. Implement robust MLOps practices to streamline model deployment, monitoring, and lifecycle management in cloud environments, including data versioning, feature store integration, and data pipeline management.
- Business Partnership \& Solution Architecture: Collaborate closely with business and engineering teams to deeply understand their challenges and customer needs, identify high\-impact opportunities to integrate AI capabilities into applications, and translate business requirements into robust cloud\-optimized application architectures, scalable data models, and technical specifications for AI\-powered solutions, considering scalability, cost\-efficiency, security, and data governance principles.
- Solution Implementation \& Delivery: Architect, implement, and deliver scalable, robust, and maintainable cloud\-native AI applications that consume and operationalize AI solutions based on defined technical specifications and architectures, ensuring seamless integration with existing systems and workflows within the Goldman Sachs ecosystem. Apply strong software engineering principles, data modeling best practices (e.g., relational, NoSQL, graph), DevOps/MLOps best practices, and cloud security standards. Drive automation of deployment, testing, and monitoring processes to ensure rapid and reliable delivery of AI applications.
- Knowledge Transfer \& Enablement: Facilitate effective knowledge transfer through comprehensive documentation, training sessions, mentorship, and pair\-programming, empowering receiving teams to take ownership and continue the development and maintenance of AI\-powered applications.
- Technology \& Innovation Leadership: Stay abreast of the latest advancements in application development, system integration, AI/ML technologies, data management platforms, and operational best practices, continuously evaluating and recommending new tools, techniques, and architectural patterns to drive innovation in AI application delivery.
Qualifications
- Bachelor's or Master’s degree in Computer Science, Software Engineering, or a related quantitative field.
- 9\+ years of hands\-on software engineering experience, with a proven track record of building and deploying robust applications, and significant experience integrating AI/ML models.
- Demonstrated experience building and deploying end\-to\-end applications that leverage LLMs and related frameworks. This includes experience with prompt engineering, API integration, and working with agentic frameworks.
- Strong proficiency in programming languages such as Python, Java, or Go, along with experience integrating with relevant AI/ML frameworks (e.g., TensorFlow, PyTorch).
- Proven ability to translate complex business requirements into well\-defined, cloud\-optimized application architectures, scalable data models (e.g., relational, NoSQL, graph), and technical specifications for AI\-powered systems, and to subsequently implement and accelerate delivery of robust, production\-ready systems based on these designs.
- Extensive experience with major cloud platforms (e.g., AWS, Azure, GCP), including cloud\-native services (serverless, containerization, managed AI/ML platforms), and a strong command of DevOps/MLOps best practices for automated deployment, monitoring, lifecycle management, data pipeline orchestration, and cloud security standards.
- Excellent communication capabilities, with the ability to articulate complex technical concepts to both technical and non\-technical stakeholders across all levels of the organization.
- Strong collaboration and interpersonal skills, with a passion for mentoring and enabling others.
- Proven ability to lead or significantly contribute to cross\-functional projects.
- Productionize LLMs: Build evaluation framework for open\-source and foundational LLMs; implement retrieval pipelines, prompt synthesis, response validation, and self\-correction loops tailored to production operations
- Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post\-incident summarization with full traceability
- Collaborate directly with users: Partner with production engineers, and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability, risk reduction, and cost; and deliver auditable, business\-aligned outcomes
- Scale and performance: Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation; leverage batching, streaming, and parallel tool\-calls to meet stringent SLOs under real\-world load
- Build agentic AI systems: Design and implement tool\-calling agents that combine retrieval, structured reasoning, and secure action execution (function calling, change orchestration, policy enforcement) following MCP protocol
- Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post\-incident summarization with full traceability
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 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
Engineering 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: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. This role's midpoint ($190K) sits 5% above the category median. Disclosed range: $130K to $250K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Goldman Sachs AI Hiring
Goldman Sachs has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $160K - $250K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.