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.

• 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.

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.

• 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, fine tuning/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, Dynamo
  
  DB, Redshift), orchestration (Step Functions), model serving (Sage Maker), and infra-as-code (Terraform/Cloud Formation).

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.

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…