Vice President AI Safety Platform Engineering

Goldman Sachs

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
New York, NY
Salary
$130,000–$250,000 / yr
Posted
9 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $199k
This role $190k
$116k most similar roles pay here $264k

This role pays more than 57% of similar roles. Most pay $157,200–$241,250 — the shaded band above. At the midpoint, this role pays about $190k versus about $199k for comparable roles.

Based on 240 similar postings.

Employer

About Goldman Sachs

Goldman Sachs is a leading global investment banking, securities, and investment management firm providing financial services to corporations, financial institutions, governments, and individuals.

Goldman Sachs currently has 134 open roles on FindRole.

Listed pay typically runs $137,000–$250,000 across 55 roles with salary data.

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At a glance

TL;DR · Vice President AI Safety Platform Engineering

As the Vice President - AI Safety Platform Engineering, you will lead dedicated engineering pods to build and oversee enterprise AI safety initiatives within the financial services sector. You will be responsible for developing a unified company-wide agentic evaluation framework, real-time LLM guardrail microservices, and automated governance controls to secure autonomous agentic systems. Your daily work involves designing multi-step reasoning assessments, implementing tool-use policy gateways, and integrating human-in-the-loop triggers for high-risk operations. You will utilize technologies including Python, Go, Java, or C++, alongside frameworks like LangChain, AutoGen, CrewAI, and OpenTelemetry. Additionally, you will integrate tools such as NeMo Guardrails, Llama Guard, and MLflow to ensure compliance with financial regulations. This role solves the critical challenge of ensuring that complex AI-driven workflows operate safely, verifiably, and within established institutional risk management standards.

What you'll do

  • Design and deploy a unified company-wide framework to benchmark and measure AI agent performance across all business lines.
  • Implement evaluation methodologies for multi-step reasoning, tool-calling precision, and automated error-recovery behaviors.
  • Build low-latency guardrail microservices to inspect prompts and outputs in real-time to prevent data leakage and policy violations.
  • Develop runtime policy gateways and human-in-the-loop triggers to authorize high-risk actions like money movement or record modifications.
  • Drive the integration of safety APIs, telemetry hooks, and MLOps pipelines into the core AI platform infrastructure.
  • Establish technical standards for immutable audit logging and execution tracing across all agentic workflows.
  • Translate financial regulatory requirements and model risk management standards into automated engineering safeguards.
  • Recruit, lead, and mentor high-performing engineering teams focused on AI safety and enterprise platform engineering.

What we're looking for

  • Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Systems Engineering, or a related quantitative field.
  • Vice President experience or equivalent senior engineering leadership in financial services or large-scale enterprise software environments.
  • 4+ years of experience leading applied ML or software engineering teams building platform infrastructure or microservices.
  • 8+ years of hands-on software development experience in Python, Go, Java, or C++ for high-throughput APIs and enterprise platforms.
  • Technical fluency with LLMs, RAG systems, function calling, and agentic execution paradigms like LangChain, AutoGen, or CrewAI.
  • Experience building agent evaluation frameworks and metrics such as LLM-as-a-Judge or trajectory trace evaluation.
  • Hands-on experience integrating low-latency guardrail tools and runtime filters like NeMo Guardrails, Guardrails AI, or Llama Guard.
  • Experience with AI observability and tracing tools including LangSmith, OpenTelemetry, Phoenix, or MLflow.

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