Init Intelligence — Senior/Staff Applied AI Engineer, Agent Harness

Init Intelligence — Senior/Staff Applied AI Engineer, Agent Harness Type: Full-time | On-site | San Francisco, CA Compensation: $200,000–$300,000 + 1%–2% equity Hiring count: 1 Visa sponsorship: Yes — H-1B, O-1, OPT Reports to: Founding team (Isaiah, co-founder, runs the culture screen) — LinkedIn not provided About Init Intelligence Init is building AI coworkers for IT teams: a security-focused product where an agent registers as a governed identity in a customer's directory, requests scoped access per task, escalates for human approval, and drives systems it was never given an API for. Init raised a $6M seed round (no product, no customers at the time), backed by Max Altman and Ben Braverman (CRO of Flexport, which sold for $2B), plus a bench of operator angels across IT and security. Several design partners today; founding team of three. Founded: 2026 | Team size: 1–10 (founding team of 3) | Total funding: $6M seed Industry: AI Tools — AI coworkers for IT Website: https://init.inc Office: San Francisco, CA Note: the company card on the role page lists the stage as "Pre-seed," while the About text describes a $6M seed round . Carried the seed figure from the fuller About text — confirm with Contrario if the stage matters. Why Candidates Should Join Ground-floor ownership: One of the first engineers, owning the core agent harness end-to-end — execution loop, tool-use strategies, context construction, evals, and the runtime-vs-compiled boundary. Serious backing for a seed team: $6M seed backed by Max Altman and Flexport's ex-CRO Ben Braverman, plus operator angels across IT and security; several design partners already live. Hard, real problems: Evals against replicas of real customer environments, per-task microVMs, model-blind credentials, and computer-use on API-less systems — production rigor, not demos. Comp + equity: $200K–$300K base with a meaningful 1%–2% equity stake at the seed stage. Intake Call Summary No intake-call transcript was included on the role page. An Intake Video is present but has no accompanying text. Update this section once transcript notes are available. The Role This role builds the layer that turns model capability into systems that actually work for users. The engineer develops the core agent harness (execution loop, tool-use strategies, context construction, model-facing experimentation) and iterates on agent behaviors across real customer workflows and long-horizon tasks. Defining stance: the creative step happens once, at authoring, and what executes afterward is deterministic, compiled, type-checked code rather than stochastic tool-chaining — and this engineer owns that boundary between what the model decides at runtime and what ships as code. What You'll Own The core agent harness: execution loop, tool-use strategies, context construction, and model-facing experimentation Agent behaviors across real customer workflows and long-horizon tasks The boundary between runtime model decisions and deterministic, compiled, type-checked execution Evals against replicas of real customer environments, reliable enough to gate releases Production failure analysis and systematic robustness improvements, attributed by layer (model, prompt, tool contract, environment state, retry logic) Extending computer-use to API-less systems, with production guarantees (per-task microVM, model-blind credentials, full action recording, killable sessions) Feedback loops and data systems that get better real-task data into eval and training Strong Candidates May Also Have built computer-use or browser-automation agents Have experience with virtualization and sandboxed execution environments, and scaling them Have done AI research and published at top conferences Have shipped something where correctness had to survive partial failure (idempotency, resumability, compensating actions) Have integrated enterprise SaaS APIs (identity providers, directory services, ticketing, ERPs) Have experience with large, messy datasets or production logs Have been an early engineer somewhere they also had to talk to customers Tech stack: Python or TypeScript; modern AI tooling; agent harness (execution loop, tool-use, context construction); model-facing experimentation; evals against environment replicas; microVM / sandboxed execution; computer-use / browser automation. Requirements Experience with agent frameworks or tool-using LLM systems Strong in Python or TypeScript, comfortable with modern AI tooling Experience with model evaluation, fine-tuning, or prompt design Can own systems end-to-end and debug across the stack Thinks in terms of systems and user outcomes, not just model metrics Enjoys debugging messy, real-world failures and turning them into improvements Wants to work in the layer that turns model capability into systems that work for users Able to work in person in SF, Monday to Friday, at startup intensity Green Flags Exceptional signal of excellence in some facet: led an important team at a high-growth company, top competition results (ICPC, IOI), or elite achievement in a sport, game, or craft Top-tier schooling (Harvard, Stanford, Berkeley, CMU, Northeastern, Georgia Tech, UIUC, UW, Waterloo, Oxford) for a CS/eng degree, OR genuinely exceptional experience in lieu of it Worked at a great company on a relevant, high-caliber team (team matters: agents/AI infra, not ads) Known for good agents in production High agency: former founders, big scope, real ambiguity Red Flags Career breaks / the "eat-pray" archetype (a year off, or three short jobs in a year) Currently "looking for work" / open-to-work with a thin, fluff-heavy profile A weak foreign university plus a US master's used to launder credentials (unless a top institution like an IIT) AI demos without production rigor; only implemented scoped features; depends on other teams for architecture, infra, or product decisions Role Details Salary — $200,000–$300,000 Equity — 1%–2% On-site policy — In person in SF, Monday–Friday, at startup intensity Visa sponsorship — Yes (H-1B, O-1, OPT) Employment type — Full-time Location — San Francisco, CA Experience — 4+ years Benefits — Meals in office, health insurance, unlimited PTO Screening Questions None provided on the role page. Add here if Contrario supplies role-specific screening questions. Interview Process Stage 1 — Pending Approval — Candidates awaiting initial approval. Stage 2 — Culture screen — With Isaiah (co-founder), ~30 min. Stage 3 — Culture screen (2) — Second culture screen (no further detail on the role page). Stage 4 — Technical interview — ~1 hour, systems-design focused. Stage 5 — One-day on-site Stage 6 — Offer Extended Stage 7 — Candidate Hired — Candidate accepts and starts. Ideal Companies & Backgrounds Not provided as a separate section on the role page. Ideal Candidate Profiles For reference only — do not source these specific profiles. (Contrario labels this section "Ideal Candidates — DO NOT CONTACT.") Elijah ben Izzy — LinkedIn URL not captured in the pasted HTML Co-created Apache Hamilton; wrote Apache Burr (990 commits, dominant author), a declarative agent state machine with replay and telemetry. Ex-CTO of DAGWorks (YC W23), acquired into Salesforce Agentforce. Asanshay Gupta — Website/profile URL not captured in the pasted HTML Published on in-context distillation (ICLR). Built Mobius, an infinitely-running agent that autonomously builds a company; won the YC and Modal Sandbox tracks at Treehacks 2026. Depth in GPU systems, HCI, and robotics; mentors FRC 1414. Builds constantly, unprompted.