Head of Engineering

Head of Engineering

Base Operations, Inc. | Washington, DC (Hybrid)

Base Operations builds AI-powered physical threat intelligence software for global enterprises and U.S. federal/defense agencies. Our platform — including Base Engine, Base Score, and Base Co Pilot  — helps security, risk, and operations teams understand and act on real-world threats faster than legacy intelligence tools allow. We’re a dual-use company: the same core technology serves Fortune 500 security teams and U.S. government customers, which means our engineering org has to meet a high bar for both commercial velocity and federal-grade rigor.

We’re a Series A company scaling fast, and this role is foundational to how we build, ship, and scale engineering as a discipline — not just as a function.

We’re hiring a Head of Engineering to own the technical backbone of our engineering organization. This is a hybrid IC/leadership role: you’ll be hands‑on enough to make real architectural decisions, structured enough to run delivery like a program, and fluent enough in modern AI tooling and workflows to help the team build with AI, not just build AI products.

You’ll report into our CEO — and will be one of the senior‑most technical leaders shaping how Base Operations’ engineering team scales. You’ll work hand in hand with our VP Product who has established and is responsible for the delivery cadence, roadmap decision framework and overall product and engineering operating model that drives our unified dual use strategy.

• Own the long‑term data architecture strategy across our threat intelligence pipelines, ensuring systems scale cleanly as data volume, data types, customer count, and federal compliance requirements grow.

• Make platform decisions (storage, pipelines, data modeling, database/data warehousing, ML/AI Ops) that balance speed, cost, and the dual‑use constraints of serving both commercial and federal customers (e.g., data segregation, CUI handling, FedRAMP/CMMC‑adjacent considerations).

• Partner with product and GRC on data governance, lineage, and security‑by‑design as the platform matures.

• Implement industry‑standard program management discipline for engineering delivery — technical debt & product roadmaps, sprint cadences, dependency tracking, release velocity tracking, and risk surfacing — without slowing the team down with process for its own sake.

• Own predictability: leadership and the board should be able to trust engineering’s commitments and timelines.

• Run cross‑functional planning with Product and GTM to align engineering output with enterprise and federal sales cycles.

• Stay technical and hands‑on where it matters — code review, architecture decisions, and unblocking hard technical problems, not just status reporting.

• Set and enforce engineering standards: code quality, testing, CI/CD, security practices, and technical documentation.

• Drive decisions on system architecture, tech stack evolution, and technical debt prioritization.

• Manage, mentor, and grow a team of engineers, including hiring, performance management, and career development.

• Build a healthy engineering culture at a stage where the team is scaling quickly — clear ownership, high trust, low ego.

• Act as the connective tissue between individual contributors and company leadership, translating technical realities into business terms and vice versa.

• Bring native fluency in modern AI development workflows — using AI coding assistants, agentic tools, and LLM‑based systems as a default part of how the team builds, not as a novelty.

• Identify where AI can…