{"id":829893,"url":"https://alion.io/job/withdaydream-applied-ai-engineer","title":"Applied AI Engineer","company":{"id":689299,"name":"daydream","domain":"withdaydream.com","url":"https://alion.io/company/withdaydream","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":160000,"max":260000,"currency":"USD","period":"year","gross":null,"usd_annual":260000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Function Calling","optional":false},{"name":"GitHub","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Replit","optional":false},{"name":"Slack","optional":false},{"name":"Tool Use","optional":false},{"name":"Webflow","optional":false},{"name":".NET","optional":true},{"name":"Anthropic","optional":true},{"name":"C#","optional":true},{"name":"GraphQL","optional":true},{"name":"JavaScript","optional":true},{"name":"NLP","optional":true},{"name":"React.js","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-05-12T17:12:07Z","employer_posted_date":"2026-05-12","last_verified_at":"2026-09-30T07:10:09Z","board_verified":true,"closed_at":null,"days_open":141,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":140},"description":"About sunbeam\nsunbeam is building a fully autonomous SEO agent.\nIt monitors rankings and competitors, finds content and technical SEO opportunities, writes and improves pages, and publishes to the customer's website.\nThe agent runs 24/7. It proactively finds work to do, prioritizes tasks, and executes across tools, messaging its manager in Slack when it needs context or approval.\nWe are not building a chatbot. We are building an employee.\nWhy us\nWe ran an SEO agency before building sunbeam. We did SEO for Clay, Replit, Hims & Hers, Rho, and others. We know the work deeply, and we are encoding that expertise into an agent that can do the job with full independence.\nWe have raised $20M+ from First Round Capital, Basis Set Ventures, WndrCo, and SOMA Capital.\nWe are starting with SEO, but the long-term goal is every acquisition channel. Marketing services is a $1T/year industry. If AI has automated coding, marketing is next.\nThe Engineering Challenge\nFully autonomous agents are still mostly unsolved.\nsunbeam has to plan and prioritize work across weeks and months without a human prompt. It has to reason from messy customer context, durable memory, live search data, CMS state, analytics, and prior decisions. It has to use tools safely, know when to act, know when to ask, recover when integrations fail, and keep improving as it learns more about the company.\nThe next years of AI will be defined by this shift from \"ask → answer\" to \"observe → act.\"\nWhat You Will Do\nBuild agent capabilities end to end: planning loops, tool use, prompts and instructions, memory, review workflows, evals, backend services, and customer-facing product surfaces.\n\nDesign safe action systems across integrations like GitHub, Webflow, Google Search Console, Google Analytics, Notion, and Slack.\n\nBuild observability and evaluation workflows so we can understand agent behavior, catch regressions, and measure whether Sunbeam is getting better at the job.\n\nDebug production agent runs from the model output all the way through database state, tool calls, customer context, and external API behavior.\n\nWork directly with customers and internal operators to turn expert growth judgment into product behavior.\n\nShip quickly across the stack. On a given week, you might change agent instructions, add an MCP tool, create a skill to manage memory, tune a review gate, and fix a frontend workflow.\n\nWhat We Are Looking For\nYou have been building AI products for 3+ years, with experience in production LLM agents, evals, tool-use systems, retrieval/memory systems, or workflow automation.\n\nYou are comfortable working across the stack and across levels of abstraction: from \"what should the agent do next?\" to \"why did this Postgres row end up in the wrong state?\"\n\nYou have high agency. You notice important problems, form a point of view, and drive them to completion without waiting for a detailed spec.\n\nYou want to understand the customer problem deeply enough to encode judgment into software.\n\nYou are excited by early-stage ambiguity, fast iteration, and a small team where everyone touches product, engineering, design, and customer problems.\n\nNice to Have\nExperience at a marketing, sales, or growth AI company.\n\nExperience building from 0 to 1 and 1 to 10 at an early-stage startup.\n\nFamiliarity with C#, .NET, TypeScript, React, GraphQL, Postgres, or MCP-style tool integrations.\n\nThe Team\nSmall team, San Francisco office, shipping every day.\nShravan (CTO), formerly an Engineering Lead at Flixed and a Software Engineer at Facebook, where he worked on mass-scale web scraping for Facebook Jobs and Meta Reality Labs.\n\nNico (Product), previously led growth at daydream and ran SEO for Anthropic, Clay, Replit, and others.\n\nVishruth (Founding Applied AI Engineer), previously a Data Scientist at Klaviyo specializing in NLP, with a research background from Cornell.\n\nDaniel (Founding Applied AI Engineer), formerly a Data Engineer on Tesla's Autopilot team and previously Lead Data Engineer at Replit.\n\nTom (Founding Designer), who previously led product design at Blockless and now leads our frontend efforts.\n\nLocation\nThis role is based in San Francisco and requires in-person collaboration five days a week. We offer paid relocation for candidates outside the Bay Area.\nCompensation & Benefits\n$180,000-$220,000 base salary range plus equity. Medical, dental, and vision insurance. Lunch on in-office days. Wellness and learning stipends. Two retreats a year. Frequent team dinners and outings.","description_format":"text","description_chars":4499,"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":["Equity","Vision insurance"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":true,"industries":["Sales & Marketing","Search Engine Optimization (SEO)","AI Agents"],"lifecycle":[{"event":"open","at":"2026-09-12T16:11:34Z"}],"liveness":{"score":8,"band":"cold","label":"Long shot","p_open":1,"p_active":0.283,"p_room":0.28,"age_days":140,"expected_fill_days":38,"reasons":["conf:14","win:tail","crowd:"],"computed_at":"2026-09-30T05:45:00Z"},"pay":{"stated_usd_annual":260000,"is_top_pay":true},"html_url":"https://alion.io/job/withdaydream-applied-ai-engineer","json_url":"https://alion.io/job/withdaydream-applied-ai-engineer.json","meta":{"generated_at":"2026-10-01T00:24:23Z","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":430,"day_limit":5000,"remaining_today":4570,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}