{"id":1250018,"url":"https://alion.io/job/klavis-founding-engineer","title":"Founding Engineer","company":{"id":10369,"name":"Klavis","domain":"klavis.ai","url":"https://alion.io/company/klavis","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Work at a Startup","truth_index":null},"role":"Backend","role_family":"Backend","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":150000,"max":200000,"currency":"USD","period":"year","gross":null,"usd_annual":200000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"ChatGPT","optional":false},{"name":"Claude Code","optional":false},{"name":"Cursor","optional":false},{"name":"Docker","optional":false},{"name":"Gemini","optional":false},{"name":"Git","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"SFT","optional":false},{"name":"Tool Use","optional":false},{"name":"TypeScript","optional":false}],"status":"live","first_seen_at":"2026-09-25T18:17:28Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-10-04T02:14:50Z","board_verified":true,"closed_at":null,"days_open":8,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":8},"description":"About Klavis AI\nKlavis AI is building high-quality agentic and coding data for frontier AI post-training.\nFrontier models are increasingly bottlenecked not just by compute, but by the quality of the coding environments, trajectories, rubrics, rewards, and verification data used to train them. We build that data layer: long-horizon coding tasks, terminal-based software engineering environments, hidden-test verification, expert rubrics, gold trajectories, dockerized environments, and agentic tool-use workflows ready for RL and SFT. We already work with multiple frontier AI labs on production coding and agentic data.\nKlavis is founded by Xiangkai Zeng and Zihao Lin. Xiangkai was a Senior Software Engineer on Google Gemini at Google DeepMind, where he built function-calling infrastructure, shipped agentic features, and co-authored the Gemini paper. Zihao was a Senior Software Engineer and Tech Lead at Lyft Recommendations ML and Nordstrom Data Infra, where he built products and infrastructure serving millions of users.\nThe role\nWe’re hiring a founding engineer who is genuinely exceptional at using LLMs and coding agents to build, test, debug, and ship real software.\nYou should be the kind of engineer who can make Claude Code, Codex, MCP tools, shell environments, custom evals, Docker, and agent pipelines feel like an extension of your hands. We are not looking for someone who has only tried basic ChatGPT prompts or simple API wrappers. We are looking for someone who already uses AI agents to build, debug, refactor, test, and ship faster than traditional engineering teams.\nYou’ll work directly with the founders to build the systems and datasets that help frontier labs train better coding and tool-use agents. In your first 30 days, you’ll onboard into our internal systems, ship improvements to them, and personally produce high-quality tasks end-to-end. By the end of the first month, you should understand what makes a task valuable for frontier post-training. In 90 days, you’ll own a core product or infrastructure area from design to production. You’ll help define what “excellent” agentic and coding data means, build systems that scale production, and directly influence how frontier labs train their next-generation agents.\nWhat you’ll work on\nBuild high-quality long-horizon coding datasets for frontier AI post-training\nCreate coding tasks, hidden tests, gold solutions, rubrics, dockerized environments, and agent trajectories\nDesign realistic software engineering workflows across terminals, repos, APIs, databases, and developer tools\nUse LLMs and coding agents aggressively to accelerate engineering and data production\nBuild infrastructure for data generation, environment orchestration, verification, evaluation, and QA\nWork with human experts to define difficult, realistic, and verifiable coding tasks\nDesign agentic tool-use workflows across SaaS apps, APIs, MCP servers, and external tools where needed\nTurn ambiguous customer needs into reliable, scalable data products\nWhat we're looking for\nHave 3+ years of professional software engineering experience\nAre extremely strong with LLMs, coding agents, and AI-assisted engineering workflows\nCan build across Python, TypeScript, shell, Docker, Git, APIs, databases, and modern dev tools\nHave strong taste for realistic long-horizon coding tasks, test design, agent workflows, evals, and edge cases\nCare deeply about correctness, verification, reproducibility, and data quality\nMove fast without accepting sloppy work\nWant the ownership, ambiguity, and intensity of joining at the founding stage\nStrong signals\nYou have created long-horizon coding benchmarks, hidden tests, task environments, coding-agent evals, or data pipelines\nYou have built custom coding-agent workflows, MCP servers, eval systems, or LLM orchestration tools\nYou use Claude Code, Codex, Cursor, or similar tools daily and deeply understand their failure modes\nYou have strong open-source, infra, systems, ML engineering, devtools, or competitive programming experience\nYou can show examples of agents helping you ship real software, not just demos\nWhy join\nWork on a core bottleneck for frontier AI: post-training data quality\nBuild products already used by frontier AI labs\nJoin a YC-backed company at the founding stage\nWork directly with technical founders with deep agentic AI, infra, and ML systems experience\nOwn important engineering and product decisions from day one\nHelp define how future AI coding and tool-use agents are trained","description_format":"text","description_chars":4504,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["AI Agents","AI Training Data & Annotation","AI Development Tools"],"lifecycle":[{"event":"open","at":"2026-09-25T18:17:28Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 1","filings_12m":1,"filings_prev_12m":0,"green_card_filings_12m":0,"median_offered_wage_usd":152500,"route":null,"cap_exempt":false,"checked_at":"2026-10-03T21:08:04+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)"],"filings_for_role_12m":1}],"liveness":{"score":50,"band":"ok","label":"Likely open","p_open":1,"p_active":0.86,"p_room":0.578,"age_days":7,"expected_fill_days":7,"reasons":["conf:23","win:tail"],"computed_at":"2026-10-03T05:45:00Z"},"pay":{"stated_usd_annual":200000,"is_top_pay":true},"html_url":"https://alion.io/job/klavis-founding-engineer","json_url":"https://alion.io/job/klavis-founding-engineer.json","meta":{"generated_at":"2026-10-04T02:17:14Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":3224,"day_limit":5000,"remaining_today":1776,"minute_limit":60,"resets_at":"2026-10-05T00:00:00Z"}}}