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About This Role
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. Drive new businesses, redefine finance using AI, and seize opportunities at market speed.
In an era defined by AI, Engineering isn't just central – it's the driving force behind our business. Our dynamic environment demands innovative strategic thinking and immediate, impactful solutions. Ready to push the limits of digital possibility? Begin your journey here.
Who We Look For
Goldman Sachs Engineers are at the forefront of innovation, driving solutions as creative collaborators in a fast\-paced global environment. We seek individuals who evolve, adapt, and thrive on challenging problems.
As an engineer within the firm's Global Banking \& Markets business, you will be instrumental in developing cutting\-edge software solutions. Your responsibilities will include designing, developing, and implementing robust, low\-latency systems and innovative platforms that enable efficient P\&L management for our trading desks.
Critically, you will lean heavily into AI\-driven development. You will be responsible for architecting and implementing production\-grade AI solutions that integrate directly into our existing Java\-based microservices. Using Goldman Sachs' AI tooling and agentic coding assistants, you will govern multiple AI agents, rapidly comprehend large legacy codebases, generate and assess production\-quality code, and accelerate every phase of the software development lifecycle.
Your Impact
- Drive strategic transformation: Work alongside domain experts to understand existing production processes, challenge long\-held assumptions that may no longer hold in a cloud\-centric, AI\-driven world, and define and deliver the requirements for efficient AI integration.
- Multiply your output with AI: Orchestrate AI coding agents (e.g., Claude Code, GitHub Copilot, Devin, Gemini Code Assist) across all stages of the SDLC while maintaining mastery, quality, and production fitness over all AI\-generated work product.
- Deliver outsized business value: Position Goldman Sachs' trading business to handle orders\-of\-magnitude higher volumes at lower operational costs, directly contributing to the firm's competitive and commercial edge.
- Build for the future: Design and implement high\-availability, multi\-region, event\-driven services on a modern cloud\-native platform, setting the architectural standard for years to come.
What You Will Do
- Design, build, and operate high\-availability, multi\-region, cloud\-native services with security and comprehensive observability (metrics, distributed tracing, structured logging) built in at every layer.
- Develop event\-driven architectures, multi\-stage processing pipelines, and optimized data paths for high\-throughput trade lifecycle management.
- Partner with engineers, domain experts, and global stakeholders to understand, model, and digitize the firm's business processes, challenging entrenched practices and driving modernization across teams.
- Manage the full lifecycle of software components, from requirements analysis through design, development, testing, and release, in line with robust SDLC and Agile best practices.
- Innovate creative solutions to complex business and technical problems, building reusable capabilities that can be leveraged across Global Banking \& Markets' front, middle, and back office functions.
- Multiply your impact with a modern, AI\-centric toolchain, orchestrating AI coding agents across all stages of the SDLC to rapidly comprehend large codebases, generate production\-quality code, and accelerate delivery.
Required Qualifications
- 8\+ years of professional software development experience in Java (Java 17\+ preferred), with strong command of concurrency, collections, and modern language features
- Demonstrated experience with AI\-assisted engineering tools (e.g., Claude Code, GitHub Copilot Agent Mode, Devin, Gemini Code Assist), including the ability to govern AI agents, critically assess their output, and maintain quality over AI\-generated work product.
- Experience building event\-driven and distributed systems, including familiarity with messaging platforms (e.g., Apache Kafka), delivery guarantees, and resilience strategies.
- Strong SDLC practices: version control, CI/CD pipelines, automated build/test/deploy workflows, and code quality tooling.
- Solid testing discipline: unit, integration, and acceptance testing with modern frameworks.
- Ability to rapidly navigate, understand, and debug large and unfamiliar codebases — with and without AI assistance.
- Excellent communication and collaboration skills across technical and non\-technical audiences in geographically distributed teams.
Preferred Qualifications
Experience with a meaningful subset of the following is highly valued:
- Frameworks \& Architecture: Spring Boot, gRPC / Protocol Buffers, integration/orchestration frameworks (e.g., Apache Camel, Spring Integration), and pipeline/adapter design patterns (retry, dead\-letter queues, error isolation).
- Cloud \& Infrastructure: Cloud platforms (GCP, AWS), container orchestration (Kubernetes, Docker), and JVM tuning for containerized workloads.
- Observability \& Operations: Application instrumentation (metrics, distributed tracing, structured logging) and production support in high\-availability environments.
- Data \& Performance: Data modeling, SQL/NoSQL databases, caching strategies, and performance optimization in latency\-sensitive systems.
- Security: Enterprise security patterns; authentication protocols, mutual TLS, secrets management, and certificate rotation.
- Domain Knowledge: Equities, post\-trade, or financial services experience; trade lifecycle concepts, asset servicing, position management, reconciliation, and multi\-system migration environments.
- Other: Asynchronous / non\-blocking I/O frameworks (e.g., Vert.x, Netty), multi\-region / BCP architectures, and open\-source contribution experience.
Salary Range
The expected base salary for this New York, New York, United States\-based position is $150,000\-$300,000\. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year\-end.
Benefits
Goldman Sachs is committed to providing our people with valuable and competitive benefits and wellness offerings, as it is a core part of providing a strong overall employee experience. A summary of these offerings, which are generally available to active, non\-temporary, full\-time and part\-time US employees who work at least 20 hours per week, can be found here.
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., 2026\. All rights reserved.
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 FUNCTIONSoftware Engineering
DIVISIONGlobal Banking \& Markets
SALARY RANGEUSD 130,000 \- 250,000
Salary Context
This $130K-$300K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $185K across 231 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Goldman Sachs, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $218,500 based on 729 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $130K to $300K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
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, Dallas, TX, US. Compensation range: $140K - $300K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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