Software Senior Principal Engineer​/Autonomous AI Software Engineering

Software Senior Principal Engineer – Autonomous AI Software Engineering

Software engineering is being reshaped by autonomous AI agents that can plan, write, test, and review code with increasing independence. Our team works at the front of the shift, bringing agent‑driven autonomy into the software development lifecycle so that engineers set direction and make key decisions while agents handle execution.

Because the agent landscape evolves rapidly, we stay hands‑on: exploring promising approaches, validating them through rigorous experimentation on real engineering workloads, and turning successful outcomes into enterprise‑grade software development practices. We are a small, AI‑native team operating with startup agility—emphasizing high autonomy, rapid iteration, and seamless human‑AI collaboration.

Join us to do the best work of your career and make a profound impact as a Software Senior Principal Engineer on our AI Governance Engineering team in Hopkinton, Massachusetts. This is an onsite role.

What You'll Achieve

You will serve as the technical leader and architect for autonomous AI across our software development lifecycle. You will define the target architecture for agent‑driven engineering, lead objective evaluations of commercial, open‑source, and in‑house technologies, and translate proof‑of‑concept results into actionable technical strategies for executive leadership.

This is a hands‑on architecture role: you will write code, build critical prototypes and integrations directly, and guide engineering teams by technical example from pilot stages to enterprise‑scale deployment.

You Will:

• Define Reference Architectures: Architect autonomous agent systems within the SDLC, encompassing orchestration, multi‑agent design patterns, tool use, Model Context Protocol (MCP), sandboxed runtime environments, model routing, human‑in‑the‑loop controls, and integration with existing engineering platforms and governance frameworks
• Lead Technical Evaluations: Maintain deep visibility into the agent ecosystem, establish objective evaluation criteria (capability, autonomy, quality, security, scalability, extensibility, and total cost of ownership), and design proof‑of‑concept tests to drive build‑versus‑buy decisions
• Own Verification and Validation: Establish the testing, qualification, and acceptance standards for agent‑generated code and artifacts, including continuous benchmarking and regression monitoring for agent platforms and underlying models
• Architect for Trust and Safety: Design frameworks ensuring auditability, traceability of agent actions, reproducibility, data privacy, failure containment, robust permission models, and mitigations against vulnerabilities such as prompt injection and unauthorized tool execution
• Drive Platform Adoption: Establish the autonomy maturity roadmap, directly construct core platform capabilities and integrations, mentor senior technical talent, and align technical initiatives across cross‑functional engineering organizations

Technologies You'll Work With:

• Agents & LLMs: Commercial and open‑source autonomous coding agents/frameworks, frontier and open‑weight models, LLM gateways, function/tool calling, Retrieval‑Augmented Generation (RAG), and the Model Context Protocol (MCP)
• Engineering & Integration: Python, Type Script / Node.js, REST and GraphQL APIs, Git‑based workflows, and modern CI/CD automation
• Verification & Testing: Automated testing frameworks, Test‑Driven Development (TDD), mutation and property‑based testing, static code and security analysis, and LLM evaluation benchmarks
• Runtime & Observability: Sandboxed container execution (Docker, Kubernetes), Open Telemetry tracing, operational metrics/dashboards, and PostgreSQL

Essential Requirements:

• 12+…