{"id":1222469,"url":"https://alion.io/job/palette-founding-dataml-engineer","title":"Founding Data/ML Engineer","company":{"id":2042016,"name":"Palette","domain":"palette.team","url":"https://alion.io/company/palette-team","size_band":"1-10","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Copenhagen, Denmark"],"countries":["DK"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Dropbox","optional":false},{"name":"Embeddings","optional":false},{"name":"GitHub","optional":false},{"name":"Google Drive","optional":false},{"name":"Linear","optional":false},{"name":"OpenAI","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Slack","optional":false},{"name":"TypeScript","optional":false},{"name":"Cloudflare","optional":true},{"name":"GitHub Actions","optional":true},{"name":"JavaScript","optional":true},{"name":"Mastra","optional":true},{"name":"Next.js","optional":true},{"name":"RAG","optional":true},{"name":"React.js","optional":true},{"name":"Redis","optional":true},{"name":"Rust","optional":true},{"name":"Tauri","optional":true}],"status":"live","first_seen_at":"2026-07-08T00:00:00Z","employer_posted_date":"2026-07-08","last_verified_at":"2026-09-29T04:12:27Z","board_verified":false,"closed_at":null,"days_open":85,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":85},"description":"Reports to: Steffen Sommer, CTO and co-founder\nAbout Palette\nFor the last decade, knowledge work drifted away from its original promise. It was supposed to be about thinking, creating, deciding. Instead it became status meetings, reporting, alignment rituals, and busywork.\nAI changes that. But the speed has created a new problem: the individual got faster than the organization. More and more work happens inside sessions between humans and agents, and the context behind that work disappears before anyone else in the company can see it.\nPalette is the shared context layer for teams and AI. We ingest signals from the tools teams already use (Slack, Linear, Notion, GitHub, calendar, email), turn them into a living map of the organization, and serve that context to the agents people work with every day.\nPalette Desktop is the app where teams put that context to work. Point it at a folder your team shares on Google Drive or Dropbox, and run agents on Anthropic, OpenAI, Mistral, or a local model inside it. More providers ship in regularly. It opens those agents up to the whole company, including non-engineers, and the team picks the model that fits. We shipped Desktop in May, and it's already where most of our own work happens.\nWe're 6 people in Copenhagen. Four co-founders, two founding engineers. We work with design partners across leading startups, scaleups, and household-name brands. We recently raised our pre-seed.\nThe role\nYou'll own the brain of Palette: the signal engine.\nToday it captures events and stores them as signals - enough for the briefs and context pages we ship now. Next is a queryable graph of activities: everything that happens across a company's tools, on one timeline, answerable by who, what, when, and meaning, by both people and AI agents. You'll drive that, end to end.\nThis is a builder role. You work AI-native - use the tools, move fast, own the output. If the AI writes 80% of the code, great; just reason about it and ship.\nThis is a founding role. You'll define how Palette turns raw signals into shared context.\nWho you are\nThe ideal person sits at the intersection of four hats:\nData engineer. You own the pipelines at real volume, and take data privacy and security seriously.\nML engineer. Embeddings, clustering, retrieval, vector search, LLMs as system components, evals.\nData scientist. You spot the use cases and drive what we build next on top of the data.\nSoftware engineer. Comfortable across the stack, comfortable in TypeScript / Postgres.\nYou won't be equally deep in all four - nobody is. But you're genuinely strong in at least one and eager to grow into the rest. Right skills, but more importantly the right mindset: strong product sense, agency, curiosity. You think in relationships and retrieval, and you care about cost and quality. Bonus points for RAG and graph-shaped data.\nThe stack\nDesktop: Tauri v2, Rust, TypeScript / React / TanStack\n Frontend: Next.js (migrating to TanStack)\nBackend: Hono\nAI agents: Mastra, plus the coding-agent harnesses we run on-device\nData & infra: PostgreSQL, Redis, Inngest, Nango, Railway\nPlus: Linear, GitHub Actions, WorkOS, Cloudflare, Sentry, Incident.io, Requesty, Swarmia, PostHog, Atlas, and more\nWe're bullish on TypeScript, but we're constantly evaluating our approach and open to being challenged.\nHow we work\nCopenhagen office. Mostly in person, but around two days from home every week.\nWe aim to stay a small and lean team.\nWe're all doers and care about our craftsmanship.\nWe're constantly exploring what AI-native means to us.\nWe're obsessed with making something people actually want to use, so everyone at the company talks to customers regularly.\nWhat you get\nTop-tier salary plus warrants\nSolid office in Copenhagen\nThe gear you need\nDental insurance\nLunch, snacks, and drinks at the office\nHow to apply\nApply on The Hub.","description_format":"text","description_chars":3850,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Dental insurance"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["AI Agents"],"lifecycle":[{"event":"open","at":"2026-09-25T12:36:19Z"}],"liveness":{"score":3,"band":"cold","label":"Long shot","p_open":0.9,"p_active":0.1,"p_room":0.28,"age_days":85,"expected_fill_days":25,"reasons":["conf:49","win:tail","crowd:"],"computed_at":"2026-10-01T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/palette-founding-dataml-engineer","json_url":"https://alion.io/job/palette-founding-dataml-engineer.json","meta":{"generated_at":"2026-10-01T12:40:05Z","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":4040,"day_limit":5000,"remaining_today":960,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}