Staff Enterprise AI Engineer

Peloton is looking to transform our enterprise tech strategy with AI adoption. We are seeking a Staff Enterprise AI Engineer to serve as the "Founding Engineer" of our Enterprise AI Platform. This role focuses on architecting and building the operating system that enables our Product, People, and Operations teams to deploy AI Agents safely and at scale. You will act as a "Player/Coach," laying the technical foundation while guiding a team of engineers to execute the vision.

Responsibilities

• Architect the "Intelligence & Integration" Layers:
  • Design and build a scalable Agentic Orchestration Platform (using LangChain, LangGraph, or custom frameworks) that allows internal developers to spin up autonomous agents.
  • Implement the "Integration Layer" ensuring all AI agents connect to internal APIs (Workday, Snowflake, SAP) via secure, standardized protocols (Model Context Protocol - MCP).
  • Solve the "State Problem" for AI, architecting memory stores (Vector DBs like Pinecone/Weaviate) that persist context across user sessions.
• Enforce "Security by Design":
  • Partner with Security leadership to implement Identity Propagation. Ensure agents execute tasks using the user’s specific OAuth scopes, preventing privilege escalation.
  • Build "Data Clean Rooms" and PII masking pipelines to ensure sensitive member or employee data is never leaked to model providers.
  • Deploy EvalOps pipelines to automatically test models for hallucination and regression before they hit production.
• Define the Engineering Standards:
  • Establish "Guide vs. Control" standards for the organization. Create templates and libraries that allow analysts to use low-code/assisted coding safely within guardrails.
  • Perform rigorous code reviews for partner teams and vendors, ensuring high performance, low latency (<200ms), and cost efficiency.
• Capital-Efficient Scale:
  • Optimize inference costs by implementing Semantic Caching and routing logic (e.g., routing simple queries to smaller/cheaper models).
  • Leverage Kubernetes (EKS) to manage ephemeral compute resources for AI workloads.

Requirements

• 10+ years of software engineering experience, with 3+ years specifically focused on MLOps, LLM Orchestration, or Large Scale Distributed Systems.
• Deep fluency in Python (production grade) and Go (preferred for platform services).
• Proven experience deploying RAG (Retrieval Augmented Generation) and Agentic Workflows in production. Experience with frameworks like LangChain, Semantic Kernel, or similar.
• Strong background in Kubernetes (EKS), Docker, and Infrastructure-as-Code (Terraform).
• Solid understanding of OAuth 2.0 (OBO flow), RBAC, and zero-trust networking principles.
• Ability to explain complex technical trade-offs (e.g., "Latency vs. Accuracy") to executive stakeholders.

Preferred Qualifications

• Experience implementing Model Context Protocol (MCP) or similar standardized tool interfaces.
• Background in FinOps (managing GPU/Cloud spend).
• Experience navigating highly regulated environments (HIPAA, SOX, etc.).

Skills

• A Systems Builder: You view AI as a distributed systems problem. You care about latency, rate limiting, and eventual consistency as much as prompt engineering.
• A Pragmatist: You build tools that solve specific business frictions (e.g., automating Content PR approvals or speeding up Supply Chain queries).
• A Force Multiplier: You enjoy mentoring senior engineers and demystifying AI for non-technical stakeholders (from HR to Product).

Benefits

• Medical, dental, and vision insurance.
• Generous paid time off policy.
• Short-term and long-term disability.
• Access to mental health services.
• 401k, tuition reimbursement, and student loan paydown plans.
• Employee Stock Purchase Plan.
• Fertility and adoption support and up to 18 weeks of paid parental leave.
• Child care and family care discounts.
• Free access to Peloton Digital App and apparel and product discounts.
• Commuter benefits and Citi Bike discount.
• Pet insurance.

Pay

Base Salary Range: $193,550 USD - $237,750 USD