Pre-seed startup in Europe building a hierarchical Bayesian Marketing Mix Modeling platform. Looking for a founding engineer who has shipped serious probabilistic programming to production, ideally with hierarchical models on commercial / marketing data. Remote EU (worldwide with EU overlap negotiable). Co-founder-level equity + cash commensurate with experience. Apply: hello@calybra.to — link to a PyMC/Stan project you’re proud of, plus a 3-min Loom on a Bayesian decision you made in production.
What we’re building. A platform that does MMM for multi-channel brands spending €3M+/year across paid media, TV, OOH, and offline. MMM is the right tool for the job — platform-reported attribution has been broken for years and is now getting worse with cookie deprecation, DMA, and AI-managed buying (Performance Max, Advantage+) — but most MMM in-market today is still delivered as consultant-led quarterly PDFs. We think the category deserves a modern product with a proprietary modeling engine, validated calibration, response curves and scenario planning surfaced directly to the marketing buyer, and analyst-in-the-loop review (we treat “a human approves the model” as a feature, not a limitation).
Prior art we’ve read and respect. Meta Robyn, Google Lightweight MMM, Google Meridian, Jin et al. (2017) on Bayesian methods for media mix modeling, and the broader literature on geo-based causal inference and hierarchical shrinkage in marketing. We’re not reinventing adstock and saturation — we’re trying to productize the best of what’s out there, make it auditable for a non-technical buyer, and close the loop with incrementality tests where clients can run them.
What you’d own.
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The core hierarchical Bayesian model: priors, group structure across clients/categories, adstock and saturation parameterization, sampler choice (we’re PyMC-first, open to numpyro or Stan where it helps).
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The validation pipeline: geo-holdout, posterior predictive checks, calibration against lift tests, identifiability diagnostics.
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The data schema and ingestion contract with our product engineer (Meta Ads, Google Ads, GA4, TikTok connectors + CSV).
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The “model confidence” surface the customer sees in the dashboard — this is our trust-building promise, not a nice-to-have.
Who we’re looking for.
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You’ve built Bayesian models in production using PyMC, Stan, or numpyro. You can talk concretely about a time you changed a prior and it actually mattered.
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You have opinions about identifiability and you’ve debugged it in a real hierarchical model with messy data.
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You’ve validated models where there is no ground truth. You know the difference between “fits the data” and “decision-relevant”.
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Bonus: MMM, media attribution, retail/CPG forecasting, or hierarchical econometrics experience. Contributions to PyMC, Robyn, LightweightMMM, or Meridian are a strong signal — we’d love to hear about them.
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Nice-to-have: comfort shipping code (not just notebooks), interest in working closely with a product engineer, and curiosity about the commercial side (why the CMO will use this).
Not what we need. A generic ML engineer who did deep learning and is curious about Bayesian. A researcher who doesn’t want to see their work in a customer’s hands. Someone who thinks an LLM can replace the model (it can’t — but it can make the output easier to act on).
Compensation. Co-founder-level equity + cash commensurate with experience. Standard 4-year vesting with 1-year cliff. We’re a small team, remote EU, full-time. Happy to discuss the cash/equity trade-off openly on the first call.
How to apply. Email hello@calybra.to (or DM me here on Discourse) with:
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Link to a repo, notebook, or OSS contribution you’re proud of.
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3-minute Loom (or audio file) on the most important Bayesian decision you’ve made in production and why.
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CV optional.
We’ll reply within 5 working days, whatever the outcome.
Happy to answer technical or process questions publicly in this thread — assume anything asked here is useful to other candidates reading it later.
-– The Calybra team
A note on who’s behind this. Calybra is led by a serial founder and investor with a track record of building and backing successful companies. We mention it because at pre-seed there isn’t much else public about us yet, and we know that matters when you’re considering a founding role.