Senior Software Engineer, AI Platform

Responsibilities

• Design, implement, and deploy production-grade AI agents: multi-step reasoning pipelines, tool-calling workflows, multi-agent coordination, and human-in-the-loop handoffs
• Engineer context pipelines: dynamic retrieval, re-ranking, semantic search, and GraphRAG as tools within an agentic reasoning loop — not static RAG pipelines; understand when to retrieve, when to use long context, and when to use agent memory
• Implement production-grade reliability: retry logic with backoff, cost controls, structured output validation, sandboxed tool execution, and checkpoint-resume for long-running agent workflows
• Develop systematic evaluation frameworks (evals, golden datasets, regression suites, observability traces) that measure agent quality and catch regressions before production
• Architect and implement scalable backend services and APIs (REST/GraphQL) in Go, Rust, or TypeScript/Node.js
• Build and maintain integrations with external systems — databases, internal APIs, robot data streams — enabling agents to take real actions with appropriate access controls
• Own deployment, monitoring, and observability: Docker, Kubernetes, CI/CD pipelines, and LLM-specific tracing and cost tracking
• Build clean, functional web interfaces in React/Next.js — operator dashboards for robot fleet management, engineering tooling for the AI team, and customer-facing applications
• Treat prompt engineering as a first-class engineering discipline: write, test, and version prompts with the same rigor as application code

Requirements

• Software Engineering Foundation 5+ years of professional software engineering experience with a full-stack production track record — this is a software engineering role first; strong fundamentals in system design, data structures, algorithms, and code quality are required
• Strong command of Python and/or TypeScript at a production level: clean abstractions, testable code, performance awareness, and maintainability — not just scripting
• Backend engineering depth: Go, Rust, or TypeScript/Node.js for production services — RESTful and GraphQL API design, relational database modeling (PostgreSQL), async programming, caching, and system integration via APIs and webhooks; Python for AI/ML integration and scripting
• Frontend engineering proficiency: React, Next.js, TypeScript — able to architect and ship functional, production-grade UIs, not just wire up component libraries
• Software delivery practices: automated testing (unit, integration, end-to-end), CI/CD pipelines, code review, and observability (logging, metrics, alerting)
• Containerization and deployment: Docker, Kubernetes — able to own a service from code to production without a DevOps handoff
• AI & Agent Engineering Proven, hands-on experience building and deploying LLM-powered systems or AI agents in production — beyond prototypes; you understand the real failure modes (non-determinism, prompt injection, tool-calling loops, cost spirals)
• Experience with at least one LLM API (Anthropic Claude, OpenAI, or equivalent) and agentic frameworks (LangChain, LangGraph, PydanticAI, or similar)
• Ability to design agent architectures with appropriate guardrails: structured output validation, retry logic, fallback handling, and human-in-the-loop patterns

Qualifications

• Familiarity with harness engineering patterns: AGENTS.md structured repositories, architectural constraint enforcement via linters, observability-driven agent iteration, and agent-first documentation as living systems — not static docs
• Understanding of context engineering beyond naive RAG: agentic retrieval, GraphRAG, hybrid search, semantic layers, and when long context windows are a better fit than retrieval
• Experience with durable execution patterns (Temporal, or similar) for long-running or stateful agent workflows with checkpoint-resume
• Vector database and embedding experience (Pinecone, Weaviate, pgvector, Voyage AI, etc.) — but as one tool in a broader context engineering stack, not the whole solution
• Background in robotics, industrial automation, or IoT — experience building software that connects to physical hardware or real-time data streams
• Experience designing multi-tenant platforms or internal developer platforms (SDKs, golden-path tooling, shared infrastructure)
• Familiarity with prompt injection risks, sandboxed code execution, and AI security considerations for agents that take real-world actions
• Active use of AI coding agents (Claude Code, Codex, Gemini, or equivalent) as a core part of your development workflow — you know how to get 10x leverage from them without shipping broken code