Senior AI Software Engineer

Peloton

1d•$160,600 - $224,950•Remote

About The Position

We at Peloton are looking for a scrappy, creative and fast-moving AI Engineer. You’ll take raw ideas and rapidly transform them into production-grade applications that empower our global organization. You will architect the Intelligence & Integration Layers that connect autonomous agents directly into Peloton’s enterprise infrastructure. We don't care about your specific stack or what programming language you grew up with. We care about systems thinking, engineering fundamentals, product instinct and your ability to ship fast.

Requirements

5–7+ Years of General Software Engineering: Strong foundational software engineering skills (distributed systems, async patterns, API design, microservices, and databases)
Language-Agnostic Mindset: Deep fluency in Python or Go, but comfortable pivoting to whatever language or framework gets the job done fastest
Production AI Execution: Hands-on experience deploying RAG (Retrieval-Augmented Generation) and Agentic Workflows into production using LLM APIs, vector databases, and orchestration frameworks

Responsibilities

Take ambiguous problems and turn them into functional, high-performing AI features and standalone tools
Implement standardized integration layers connecting agents to core enterprise systems using secure protocols like MCP
Solve the "State Problem" for AI by building memory architectures and vector stores (Pinecone, Pgvector) that maintain context while strictly adhering to data retention and deletion policies
Partner with Security to implement Identity Propagation (e.g., OAuth On-Behalf-Of flows) and RBAC. Ensure agents execute tasks strictly using the user’s specific permissions, eliminating privilege escalation risks
Build "Data Clean Rooms" and automated PII masking/redaction pipelines to guarantee that sensitive member, employee, or proprietary financial data is never exposed to external model providers
Ensure all AI workflows comply with enterprise regulatory standards and internal data governance policies
Deploy continuous evaluation pipelines to test models for hallucinations, prompt injections, data leakage, and performance regressions before code hits production
Implement Semantic Caching and smart routing logic (directing routine queries to smaller, cheaper models) to maintain low latency and efficient inference costs
Participate in creating templates, SDKs, and libraries that enable non-engineers and analysts to "vibe code" and prototype safely without bypassing compliance or security controls