Founding Engineer — Kiloforge

Kiloforge — Founding Engineer Type: Full-time | On-site (5 days/week) | San Francisco, CA Compensation: $180K–$225K + 1.0–1.5% equity Hiring count: 3 Visa sponsorship: Yes — visa transfers available; relocation support available Reports to: Nate Tucker, Founder / CEO About Kiloforge Kiloforge is building a new paradigm for software creation: a "company factory" that uses AI agents to autonomously create and operate thousands of specialized software products, solving problems for niche communities that previously couldn't access quality software. The company was founded in 2026 and raised $5.5M from a16z and other top-tier investors. Founded: 2026 | Team size: 4 | Total funding: $5.5M Industry: AI, Software Development, Devtools Website: kiloforge.com Office: SoMa, SF Why Candidates Should Join Groundbreaking mission: Building the infrastructure for AI-first company creation — an entirely new category, not an incremental product. Proven founding team: Backed by a16z; founders have previously scaled companies to $20M ARR. Maximum early-stage impact: Ship micro apps weekly to real users as one of the first 5–7 engineers, with direct access to the founding team. Generalist scope: Own full-stack work across ideation, shipping, and automation — no siloed roles, broad learning across multiple domains. Competitive early-stage comp: Up to $225K salary with 1.0–1.5% equity — meaningful upside at a very early stage. Intake Call Summary Kiloforge is described as a "company factory" — building autonomous AI organizations using agentic systems; raised $5.5M. Founding engineers will build and ship micro apps weekly with real users, and encode those processes into increasingly automated workflows until fully automated. Backend engineering experience preferred; specific language is flexible. Experience with agentic development and AI tools is the core requirement. Ideal backgrounds: former YC or a16z founders; entrepreneurial spirit and a track record of shipped products. Team structure: Only engineering hires in the first wave (5–7 engineers total); all report directly to Nate Tucker. Compensation: $180K–$225K salary with 1.0–1.5% equity. Fully on-site in SF SoMa office; visa transfers and relocation support available. Hiring pace: Two hires completed in the first four weeks; aiming for quick but selective decisions. Initial hiring ramps down after reaching five engineers. Culture: High-agency, startup-minded, innovation-oriented. Values builders and founders, not just strong technicians. Alignment with the mission and culture is as important as technical skill. Pain point: High competition for top candidates in the current market. The Role Kiloforge is hiring exceptional generalist founding engineers — full-stack, AI-fluent, and scrappy — who love building things from zero. You will ship micro apps weekly to real users, encode your processes into automated workflows, and run rapid experiments with the founding team. What You'll Be Doing Build and ship micro apps every week with real users, encoding your processes into increasingly automated workflows until fully automated. Work in close collaboration with the rest of the founding team. Run rapid experiments and decisively double down on what works while cutting what doesn't. Tech stack: Full-stack, AI (Python, JavaScript, Kotlin, Go — flexible on language) Qualifications Seniority 1–5 years of experience as a software engineer with experience building AI-driven products [Required] Work Experience Experience shipping products or features from ideation to real users with significant traction [Must have] Previous founder or early-stage startup experience strongly preferred; bonus for YC or a16z alums [Strongly preferred] Education Bachelor's degree in Computer Science or a related technical field [Required] Degree from a top-tier university (e.g., Berkeley, Stanford, CMU) [Strongly preferred] Hard Skills Proficiency in agentic development: specifically, designing and building orchestration pipelines across multiple agents, tools, and services [Must have] Full-stack proficiency with a backend focus (Python, JavaScript, Kotlin, Go all acceptable) [Required] Active user of modern AI tooling (e.g., Claude Code, Cursor, etc.) [Required] Miscellaneous Must have at least one clear signal of excellence: hackathon wins, side projects with significant traction, impressive fellowships, YC founder, etc. [Must have] Traits to Avoid Pretraining or fine-tuning ML researchers — looking for applied agentic builders, not model trainers Pure Big Tech career with no side project, startup, or scrappy chapter History of short tenures (<1 year) at multiple companies Role Details Salary$180K–$225KEquity1.0–1.5%On-site policy5 days/week in SoMa, SF; relocation support availableVisa sponsorshipYes — transfers availableEmployment typeFull-timeLocationSan Francisco, CA Screening Questions Describe an agentic system you've built. What were the agents, how did they coordinate, and what was the outcome for the end user? Where do you see your career going over the next 5 years? Can the candidate be on-site in SF? If not, are they willing to relocate? What is their salary expectation? How actively are they recruiting? Interview Process Stage 1 — Submit Candidate After submitting, you will be notified if the hiring manager wants to proceed. Stage 2 — Intro Screen (45 mins) Conversational video call. Assessing technical depth, past projects shipped end-to-end, judgment, and founder fit. Stage 3 — Pair Coding (60 mins) Live coding — no AI tools allowed. Problem: implement K-means clustering from scratch. Assessing CS fundamentals, problem-solving without crutches, and ability to reason through algorithms. Candidate prep reminder: When confirming next steps after submission, remind candidates that Stage 3 is a live coding session with no AI tools permitted — even though the role itself is heavily AI-native. Candidates should be prepared to code from scratch and reason through algorithms unaided. Stage 4 — Onsite (~4 hours) Three components: (1) agentic coding session — build something real with AI tooling; (2) system design; (3) behavioral/founder-fit conversation. Assessing how they actually work day-to-day, architectural thinking, and alignment with how the team operates. Stage 5 — Offer Extended Stage 6 — Candidate Hired Full bounty paid when candidate accepts and starts. Ideal Companies & Backgrounds Updated May 1, 2026 Top-Tier Fellowship / Accelerator Programs a16z speedrun, Y Combinator, South Park Commons High-Growth AI Startups & Companies Known for Top-Tier Engineering Talent (internships included) Decagon, Glean, Perplexity, Airbnb, Anysphere, Vercel, Stripe, Sierra Nevada Corporation, Hebbia, Cursor, Replit, Mercor, Modal Labs, Pinecone, Scale AI, LangChain, Figma, Databricks Ideal Candidate Profiles For reference only — do not source these specific profiles. Arda Akman — LinkedIn Ex Delve | Thunder | Berkeley, CA Founder DNA: co-founded ObviousAI and won Universal Ventures pitch competition Founding engineer at Delve (YC24) True generalist: data pipelines, public APIs, and security at Sigma Computing; Berkeley CS Jayaditya Sethi — LinkedIn Mandolin | UC Berkeley CS | Building in AI | San Francisco Bay Area Big tech startup trajectory Owned a full vertical at Mandolin Built production agent pipeline with confidence-thresholded LLM routing; Berkeley CS Ethan Jagoda — LinkedIn Currently building in stealth | Berkeley CS | San Francisco Bay Area Codepoint Fellow founding engineer trajectory Co-founded Tiny Dorm music community at Berkeley (2.5 years) Full-stack + ML across SHV portfolio (Observe, Sutter Hill Labs); Berkeley CS Rejected Candidate Feedback End-to-end product ownership: Ensure candidates demonstrate founder-level product ownership and have shipped complete products with real user traction — not just ML experiments. Agentic development experience: Prioritize candidates with clear applied agentic development experience across full-stack systems; reduce focus on pure ML research or model training backgrounds. Elite signals: Seek candidates from top-tier backgrounds or with standout external signals (YC, hackathon wins, side projects with traction) that align with a high-agency startup environment. Individual rejections to date: "Portfolio or GitHub quality is weak" (after HM review); "Not enough experience" (after HM review); "Skills mismatch" (after HM review); multiple "Miscellaneous" rejections (after HM review).