Engineering-L2- Menlo Park-Vice President-AI / ML Engineering Menlo Park - - Vice President

Overview

Vice President, Enterprise Technology Operations (ETO) – Production Runtime Experience (PRX) team, focused on applying software engineering and machine learning to production management services and workflows.

Responsibilities

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. Engineer robust guardrails for safety, compliance, and least-privilege access.

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; deliver auditable, business-aligned outcomes.

Safety, reliability, and governance : Build validator models, adversarial prompts, and policy checks; enforce deterministic fallbacks, circuit breakers, and rollback strategies; instrument continuous evaluations for usefulness, correctness, and risk.

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 a RAG pipeline : Curate domain knowledge; build data-quality validation framework; establish feedback loops and milestone framework to maintain knowledge freshness.

Raise the bar : Drive design reviews, experiment rigor, and high-quality engineering practices; mentor peers on agent architectures, evaluation methodologies, and safe deployment patterns.

Qualifications

A Bachelor's degree (Masters / PhD preferred) in a computational field (Computer Science, Applied Mathematics, Engineering, or related quantitative discipline), with 7+ years of experience as an applied data scientist / machine learning engineer.

Essential Skills

7+ years of software development in one or more languages (Python, C / C++, Go, Java); strong hands-on experience building and maintaining large-scale Python applications preferred.

3+ years designing, architecting, testing, and launching production ML systems, including model deployment / serving, evaluation and monitoring, data processing pipelines, and model fine-tuning workflows.

Practical experience with Large Language Models (LLMs) : API integration, prompt engineering, finetuning / adaptation, and building applications using retrieval-augmented generation (RAG) and tool-using agents (vector retrieval, function calling, secure tool execution).

Understanding of different LLMs, both commercial and open source (e.g., OpenAI, Gemini, Llama, Qwen, Claude).

Solid grasp of applied statistics, core ML concepts, algorithms, and data structures to deliver efficient and reliable solutions.

Strong analytical problem-solving, ownership, and urgency; ability to communicate complex ideas simply and collaborate effectively across global teams with a focus on measurable business impact.

Proficiency building and operating on cloud infrastructure (ideally AWS), including containerized services (ECS / EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift), orchestration (Step Functions), model serving (SageMaker), and infra-as-code (Terraform / CloudFormation).

Your Career

Goldman Sachs is a meritocracy where you will be given all the tools to advance your career. Our in-house training program, "Goldman Sachs University," offers a comprehensive series of courses that span technical, business and leadership skills.

Salary

The expected base salary for this New York, New York, United States-based position is $150,000-$250,000. 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. A summary of these offerings, 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.

Healthcare & Medical Insurance : We offer medical, dental, disability, life, travel and other related insurances and programs.

Vacation : Competitive policies with generous vacation entitlements and a minimum of three weeks of vacation usage each year.

Financial Wellness & Retirement : Retirement savings support and education; opportunities for higher education assistance and financial planning resources.

Health Services : Medical advocacy, EAP counseling, global medical and security travel assistance, on-site health centers where available.

Fitness : On-site fitness centers where available; members may be reimbursed for fitness club memberships or activities.

Child Care & Family Care : On-site child care centers and family resources; adoption, surrogacy, and related stipends may be available.

Benefits at Goldman Sachs : Read more about the full suite of benefits offered by the firm.

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