Manager, Software Engineering

Invoca is hiring an Engineering Manager to lead its AI Platform team, which builds shared infrastructure for AI-powered products and agentic workflows. The role owns team growth, platform adoption and trust, and predictable delivery while promoting AI-assisted engineering across R&D.

Responsibilities:

• Manage, coach, and develop the engineers on the AI Platform team: clear expectations, honest feedback, real career growth
• Hire senior talent in a competitive market, and own candidate experience that top-tier passive candidates say yes to: tight loops, prepared interviewers, fast decisions
• Build a culture of ownership and craft, where engineers use the agentic tools they build
• Treat the platform as a product with internal users: define adoption goals, instrument them, and measure whether teams are actually better off because of your team's work
• Partner with the Engineering and Product teams and architects on roadmap and priorities, and represent the platform in R&D planning
• Favor paved paths and shared standards (ADRs, templates, reference implementations) over one-off, team-specific solutions
• Own delivery health using DORA-style metrics and flow analysis. Our own analysis shows cycle time variation is driven mostly by queueing, not ticket difficulty; we expect you to manage flow and WIP, not just push effort
• Make delivery predictable: honest estimates, visible risks, no surprise slips
• Identify where high-volume, low-difficulty toil concentrates, and automate it
• Ship access controls, audit trails, and policy guardrails as first-class platform capabilities, not afterthoughts. Platform trust is the product
• Embed operational rigor: incident management, on-call health, observability, and honest postmortems
• Partner with Security and Infrastructure so governance keeps pace with capability
• Hold high engineering standards, maintain platform SLOs, and make it easy to adopt and scale with a trustworthy platform
• Make your own team the reference implementation of AI-assisted engineering: agentic coding workflows, automated review gates, AI in the SDLC as habit rather than demo
• Drive adoption of AI development tooling across R&D, moving teams from awareness to daily use, with an operator's playbook rather than a mandate

Requirements:

• 8+ years in software engineering, including meaningful hands-on years building backend, platform, or infrastructure systems, and 3+ years managing engineers
• Experience leading a platform, infrastructure, or developer-experience team whose customers are internal engineers, with a track record of driving measurable adoption
• Working fluency in modern AI systems: LLM APIs, agent orchestration, MCP or similar integration protocols, evaluation, and telemetry. Enough depth to hold a technical bar; you don't need to be a researcher
• Data-driven delivery management: you've used DORA metrics or equivalent to find and fix real bottlenecks, and you understand flow
• Operational and security mindset: you've owned production services, run incident response, and treat access control and auditability as design requirements
• Demonstrated success hiring and retaining senior engineers
• Direct, calibrated communication with engineers and executives alike: honest characterization over spin, risks surfaced early
• High ownership in ambiguous, fast-moving territory