Founding Engineer, June

SecureVision•Tysons, VA

About The Position

SecureVision Embedded RPO is posting this job for June. June is hiring a 10x Founding Engineer to own technical delivery and architecture for our AI-assisted hiring platform. You’ll work hands-on in the backend, partner closely with product, and be the person who clears urgent customer issues in messy real-world environments while raising the bar on reliability, security, and design as we scale. June is at an early and critical stage, onboarding several enterprise customers, and we will start fundraising for our Seed round in the next few months. We are looking for a Founding Engineer with a strong entrepreneurial desire to make an impact at the ground level and build a category defining product.

Requirements

Experience as tech lead / staff / principal or founding engineer on a product team.
Strong Python backend skills and deep comfort with relational databases (schema design, migrations, query performance, transactions).
Proven track record shipping and operating production systems; calm under pressure when customers are blocked.
Hands-on GCP (or comparable cloud) experience: IAM, networking basics, managed databases, secrets, observability.
Interest or experience in integrations (REST APIs, webhooks, ATS/recruiting tools a plus), real-time/voice/SMS stacks a plus.
Clear communication with non-engineers (customers, leadership) when diagnosing issues and scoping fixes.

Nice To Haves

Experience with Firebase Auth, Twilio, OpenAI/LLM products, vector search, or multi-tenant SaaS.
Prior exposure to security questionnaires, SOC2 controls, or privacy-conscious design for HR/recruiting data.

Responsibilities

Define and evolve the system architecture (services, data model, integrations, async work, observability). Make pragmatic tradeoffs between speed and durability for an early-stage company.
Diagnose and fix production and integration issues (ATS sync, telephony/messaging, AI pipelines, auth, cloud networking). Turn incidents into lasting fixes (runbooks, tests, monitoring).
Ship and refactor core APIs and workflows in Python/FastAPI, Postgres, Redis, background jobs/tasks, with strong attention to correctness, performance, and maintainability.
Operate comfortably in GCP (e.g., App Engine, Cloud SQL, IAM, secrets, queues). Improve deployment confidence, cost awareness, and operational hygiene.
Apply sound defaults for secrets handling, access control, data handling, and vendor integrations; partner on customer security reviews when needed.
Raise the bar for the whole team—reviews, patterns, and lightweight documentation that speed everyone up.
Design and iterate on LLM-powered workflows (prompting, tooling, evaluation harnesses, guardrails, cost/latency tradeoffs) so AI features are reliable in production, not just impressive in demos.
Own monitoring, regression detection, and safety for AI paths—structured logging around model calls, failure modes, retries/timeouts, versioning of prompts/agents, and measurable quality over time (evals, sampling, human-in-the-loop where appropriate).