AI / ML Engineer (contractor, validation phase)

We’re nanoSpec, building nanostructured SERS substrates + an AI workflow for PFAS screening. We’re looking for an AI/ML profile to help set up a clean data pipeline, baseline models, and validation metrics. Start as a paid/contract scope (4–8 weeks), option to extend if it’s a strong fit.



Must-have:

  • Python, data handling, ML basics

  • Comfortable with messy experimental data

  • Agent/ML experience



Nice-to-have:

  • spectroscopy/chemometrics, signal processing

Humboldt-Universität zu Berlin

Alexandre Chicharo

Alexandre Chicharo

Postdoc

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About the job

nanoSpec is building nanostructured metamaterials plus an AI workflow to make chemical sensing faster and more reliable, starting with PFAS screening. This role is hands-on and outcome-driven. The goal is to move from “promising lab signals” to a repeatable validation package that can convince partners, customers, and funders. In the first weeks, you can expect to work with us on: • turning experimental data into a clean, versioned dataset (formats, metadata, QA) • defining the validation plan and metrics (LOD, reproducibility, calibration, robustness) • building baseline models / analysis pipeline (chemometrics + ML where it makes sense) We are early-stage and fast-moving. You’ll have real ownership, direct access to the founders, and a lot of autonomy, but also ambiguity. We want someone who can drive work independently and communicate clearly.

• A trial-first collaboration, start with a defined scope (e.g., 4–8 weeks) so both sides can test fit • Flexibility on structure: freelance/contract now, potential evolution to a larger role later if it works well • Real technical ownership, you help shape the validation strategy and the first product narrative • Access to lab partners and real experimental data, not toy problems • Potential equity discussion after the trial, based on contribution and commitment (not promised upfront) • Serious focus on building something fundable and sellable, not endless research Practical note: we’re pre-seed, so we optimize for impact per euro and we prioritize partners who want to build, not just advise.

About the startup project

Information about the start-up project

nanoSpec Technologies is a Berlin-based deep-tech startup building nanostructured SERS substrates and an AI-driven analysis workflow to make chemical sensing faster, more reproducible, and easier to deploy outside the lab. Our first validation focus is PFAS screening (public health and regulatory relevance), starting with a substrate-first approach so labs can adopt it without changing their instrumentation. Over time, we plan to expand into a broader sensing platform across multiple chemical targets. We are an early-stage team with strong nanofabrication and sensing expertise, currently working with academic lab infrastructure for measurement and validation. We’re now looking for practical partners and team members to accelerate validation, data generation, and commercialization readiness (dataset quality, reproducibility, pilot use cases, and a clear path to market). https://www.nanospec.tech

Location of the start-up project.

Berlin