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ML Engineer — demand forecasting

Picnic

Full-time
Senior
Supply ML
Hybrid
Fred Roeskestraat 100, 1076 ED Amsterdam, Netherlands · Hybrid
EUR 78k–105k/year + equity

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

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At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: Fred Roeskestraat 100, 1076 ED Amsterdam, Netherlands · Hybrid
Compensation: EUR 78k–105k/year + equity
Experience level: Senior
Team: Supply ML
Benefits & perks: Equity, Dutch grocery tech, hybrid.
How to apply: Forecasting or route optimization ML at scale.
Company site: picnic.app
Listed: Jul 9, 2026
Updated: Aug 3, 2026

About this role

What the team is looking for and how this position fits.

Picnic is hiring a Senior ML Engineer — demand forecasting on the Supply ML team (Hybrid · Amsterdam, Netherlands). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Picnic reimagines grocery delivery with forecasting, routing, and warehouse automation in the Netherlands.

Supply ML teams optimize demand forecasts and logistics with production ownership.

What you will do

Ship production demand forecasting and logistics with offline evals, monitoring, and safe rollout.
Work with product and domain experts on requirements and trade-offs.
Improve data pipelines, labeling, and metrics tied to business KPIs.
Document runbooks for on-call and customer-facing teams.

What we look for

Strong Python and modern ML/LLM stack with shipped features.
SQL, experimentation, and clear communication to stakeholders.
Eligible to work in the listed country; professional English required.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
SQL
Analytical SQL for features, cohorts, label quality, and experiment analysis at scale.

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

AirflowTool
Scheduled ML pipelines with SLAs, backfills, and data-quality checks between stages.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Forecasting or route optimization ML at scale.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, SQL, and Airflow.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Amsterdam, Netherlands)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: EUR 78k–105k/year + equity, benefits in At a glance) and your expectations align.

Apply

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What the employer asked for

Forecasting or route optimization ML at scale.

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