Manager Software Engineering, AI Agents

Job Summary

About the Role We are looking for a hands on Software Engineering Manager to lead a backend engineering team building production grade AI Agent systems. This role sits one level above Senior Software Engineer (E4) and combines people management, execution ownership, and direct technical contribution. You are expected to remain deeply technical, spending at least ~30% of your time coding in the same production systems as your team.

You will design, build, review, and operate backend services while also being accountable for team health, delivery, and growth. This is not a “manager only” role. Success comes from leading through context, example, and hands on involvement, while scaling your impact through strong people leadership. You will partner with product, design, and leadership to turn ambiguous AI opportunities into dependable, customer facing systems—while building a high performing team that can sustain this work overtime.

• Directly manage a team of backend engineers, owning 1:1s, feedback, performance management, and career development.
• Coach engineers on technical growth, system design thinking, and operational ownership.
• Build a culture of trust, accountability, and continuous improvement—especially important in AI‑heavy, probabilistic systems.
• Hire and onboard engineers who strengthen both technical capability and team dynamics.

• Actively write and ship production code across core backend services and agent workflows.
• Take ownership of well‑scoped technical areas or services, especially those critical to correctness, reliability, or cross‑team interfaces.
• Model high standards in code quality, testing, observability, and operational readiness.
• Stay close enough to the codebase to make informed architectural, staffing, and execution decisions.

• Guide architectural decisions by contributing designs and implementations—not just reviewing others’ work.
• Ensure AI agent behavior is engineered responsibly: tool invocation, state management, memory strategies, reasoning control, failure handling, and evaluation.
• Treat prompts and agent policies as production code: versioned, tested, reviewed, and monitored for cost, latency, and reliability.
• Set and enforce engineering standards that keep AI‑heavy systems understandable and supportable over time.

• Own planning, prioritization, and execution for the team.
• Translate product goals into achievable technical plans while managing tradeoffs across scope, quality, and risk.
• Identify delivery and technical risks early and address them proactively.
• Ensure the team consistently delivers production‑ready systems, not just proofs of concept.

• Lead the team in designing testing and evaluation strategies for non‑deterministic AI components.
• Establish guardrails and regression testing that reduce unsafe or unstable behavior in production.
• Balance innovation with correctness, observability, and long‑term maintainability.

• Share ownership of production health: uptime, latency, incidents, and post‑incident learning.
• Help build a strong on‑call culture where engineers run what they build.
• Step in directly during complex incidents when needed—both technically and as a leader.

• Partner closely with product, design, and adjacent teams to align on goals and dependencies.
• Communicate clearly with leadership on progress, risks, and outcomes.
• Represent the team in technical and organizational forums.

• Prior experience as a technical lead or manager, with a demonstrated track record of mentoring and project delivery.
• A genuine interest in growing people—not just systems.

• Extensive experience designing, building, and operating backend systems in Python.
• Comfort working with asynchronous frameworks (e.g., FastAPI, asyncio) and distributed architectures.
• Ability and desire to remain hands‑on with real production code.