Member of Technical Staff, Founding Engineer

Member of Technical Staff, Founding Engineer — Forward-Deployed & Core Platform Type: Full-time About Hussh Hussh is an AI-first research, machine-learning, and product organization. We are building Private Agent One: a private personal AI agent — personal superintelligence that works for one human, and one human only. The architecture is the point: the human owns their information, controls it, and shares it only by explicit consent, through our PCHP (Personal Consent Handshake Protocol). No surveillance model. No ads against the user. The human is the customer, never the product. We also run a permanent-capital asset-management business (Fund A). The lab works from Kirkland, WA, with research partnerships at IIT Kharagpur and Stony Brook's AI3 institute. We are founder-funded. You will work directly with the founder and the founding team — there is no layer between you and the hardest problems. The role You are the engineer who does both halves of the hardest job in a young lab: you build the core platform, and you take it into the real world and make it work there. Half of this role is core platform and tooling — the agent runtime, the consent-gated data plane, identity and trust primitives, and the developer infrastructure every other engineer at Hussh will build on. The other half is forward-deployed: you embed with the people who use Private Agent One — individuals, family offices, advisors, partners — understand their actual problem in the room, build the solution with them, ship it on their infrastructure and ours, and stay until it works. Then you carry the field's truth home and turn one-off deployments into platform, so the second deployment of anything is boring. There is no "throw it over the wall" here, in either direction. You own problems end to end: discovery, scoping, design, build, deployment, operation, and the fix at 3 a.m. What you will do • Own the design, architecture, and implementation of the Private Agent One core platform: agent runtime and orchestration, memory, tool use, and the consent layer (PCHP) that governs every movement of a person's information. • Build the platform services the product runs on — identity, delegation and permissions, encrypted data planes, sync across devices and cloud — designed for billion-user scale from the first line, not retrofitted later. • Embed with users and partners to scope and deliver deployments: discovery, technical design, hands-on build at their side, production rollout, and operation after go-live. • Build the internal tooling that makes the whole lab faster: eval harnesses, deployment automation, observability, and the paved roads every engineer here ships on. • Turn field findings into platform abstractions and product feedback that visibly change the roadmap. • Set the engineering bar by example: design reviews, code reviews, tests green and a real human walkthrough of every flow. Never tests alone. Expectations • You ship. Working software in front of real users, on a cadence measured in days, not quarters. • You are an engineer first and a trusted advisor second — in that order, and both for real. Users trust you because your systems work. • You write clearly. Scopes, design docs, and field reports that a product team can act on without a follow-up meeting. • You treat sales, customer service, engagement, and empathy as engineering requirements. A deployment the user doesn't love is a failed deployment, whatever the code did. • You will generate outcomes worth 100× your pay. That is the founding bar, stated plainly. Required qualifications • 10+ years of professional software engineering experience, building and operating production systems. • The last 5 years building AI products — and product depth in at least one of: personal information systems, subscriptions, ads, or relevance/ranking. You understand how these systems make money and how they fail the user. • A track record of building for very large scale: systems serving millions of users, or platform infrastructure that does. Billion-user scale is the design bar here; your experience must be on that trajectory and you must be able to prove it in numbers — load, failure modes, tail latency. • Production depth across the stack: backend services, data systems, and at least one native client platform. You debug across boundaries instead of escalating at the edge of your layer. • Customer-facing engineering experience: you have scoped, built, and shipped with real customers in the room, and owned the result after launch. • Bachelor's degree in computer science, engineering, mathematics, or a related field — or equivalent experience demonstrated in shipped systems. Technical requirements • Languages: expert in at least two of Python, TypeScript, Go, Rust — and able to read and ship Swift and Kotlin. • Client platforms: native building across iOS and macOS (Swift/SwiftUI), Android (Kotlin/Jetpack Compose), web (React/TypeScript), Chrome extensions, and Windows. Our agent lives on every surface the user does. • Backend and data: distributed services (gRPC/REST), PostgreSQL, Redis, Kafka or equivalent streaming; data modeling for consent-scoped, encrypted, per-user data planes; end-to-end encryption and key management. • Agent systems: LLM APIs and on-device inference (llama.cpp, MLX, Core ML, ONNX Runtime); tool use and function calling; retrieval (embeddings, pgvector/Qdrant-class vector stores); agent evaluation harnesses and regression testing of model behavior. • Cloud and infrastructure: production systems on at least one hyperscaler (AWS, GCP, or Azure); Kubernetes, Terraform, Docker; CI/CD (GitHub Actions or equivalent); observability with OpenTelemetry, Prometheus/Grafana, Sentry-class tooling. • Security: OAuth 2.0/OIDC, SSO/SCIM integrations, least-privilege design, threat modeling for systems that hold a person's entire information life. Compensation Base salary: $240,000–$340,000 USD per year, plus a founding equity grant (4-year vest). Where you land in the range depends on experience and demonstrated scale. Founding titles are permanent: "Founding Engineer" is a fact about when you showed up, and no future re-leveling takes it away. Benefits Comprehensive health, dental, and vision coverage. A 401(k) plan. Employer-matched contributions to Trump Accounts. Support for continuing education, retooling, and retraining — including AI courses, which we actively encourage. Above all: you will work with the latest AI technologies every day, and the expectation — for engineers and non-engineers alike — is to become expert in using them, running evals on your own work until it reaches an A+ standard. Benefits are subject to the terms of the applicable plans and to eligibility requirements, in accordance with federal and state law. How to apply Apply here on LinkedIn with your resume and a short note answering one question: tell us about a system you deployed with a real customer that almost failed — what you did in the room, and how it ended, in numbers if you have them. We read every note ourselves. Hussh is an equal opportunity employer. We hire on merit, competence, and qualifications, and do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, veteran status, disability, or any other legally protected characteristic. To be considered — the full application: Apply here on LinkedIn, AND send the following to View email address on click.appcast.io with the subject line: [Your Full Name] — [Role Title]. (1) Your answers to the three questions below — short, specific, numbers where you have them. (2) Your profile packet: your portfolio of work and accomplishments, your resume, and a cover letter. Applications without the email packet are not considered. 1. Tell us about the hardest system you shipped end-to-end — your role, the constraint that nearly killed it, and the metric that proved it worked. 2. Our agents act only with human approval. Describe a time you built a guardrail, approval step, or undo into a product — what it prevented, and what it cost in speed. 3. Give a number you owned — latency, cost, uptime, or conversion: where it started, where you took it, and how.