Scientific Product Manager

present of AI; Amsterdam; Hybride; EUR 60 000 - 90 000; Plein temps

Résumé du poste

present of AI is seeking a scientist to connect partner laboratories to its AI materials platform, ensuring experiments, data and workflows feed automated improvement loops.

You translate lab needs into precise requirements for engineers, verify results, and serve as the main scientific contact with outside labs.

A hands-on automated or high-throughput lab background and Python skills help maintain data quality, provenance and trustworthy campaign designs.

Qualifications

• Hands-on automated or high-throughput lab experience.
• Ability to translate lab needs for engineers.
• Strong understanding of data quality and provenance.
• Proficient Python scripting.

Responsabilités

• Connect partner laboratories to AI materials platform as primary scientific contact.
• Translate lab needs into precise requirements for engineers and check results.
• Ensure data quality and provenance for experiments feeding the loop.
• Draft and verify experiments against automated improvement loops.
• Collaborate with external labs to align on workflows and outcomes.

Connaissances

Automated lab experience

Python

Data quality

Description du poste

This role is about getting partner laboratories connected to an AI materials platform, so that their experiments, data and workflows can feed automated improvement loops. It asks for a scientist with hands-on automated or high-throughput lab experience who can turn lab needs into clear requirements for engineers and check that the result works. A strong candidate knows how experimental data goes wrong, writes some Python, and is comfortable working with outside labs as the main scientific contact.

How it fits the AI era

AI is moving experiments from one-off trial and error toward loops where models suggest the next experiment and the lab reports back. That makes clean, well-documented data and realistic campaign design as important as the models themselves. What stays human is judgment about what a lab can truly do, what counts as a meaningful result, and the trust built with partner scientists.

How it builds AI experience

The work sits right next to optimisation methods such as Bayesian optimisation, applied to real lab limits like noise, failed samples and limited throughput. Someone would learn how a materials search platform is fed by real experiments and how its results get checked against manual methods. Anyone short on this can build it by running a small design of experiments project or practising on lab data with Python.

Worth showing

• Automated or high-throughput lab experience
• Experimental data quality and provenance
• Partner-facing scientific communication

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About CuspAI

An AI search engine for new materials that the company says turns a wanted set of physical properties into synthesizable chemical designs in months instead of years of lab trial and error.