Machine Learning Researcher

Alexander Daniels Global; Düsseldorf; Remote; EUR 80.000 - 100.000; Vollzeit

Zusammenfassung

A pioneering AI company in Germany is seeking a dedicated ML Research Engineer to develop AI capabilities that enhance manufacturing efficiency and sustainability. You will collaborate on technical decisions and implement algorithms in a remote-first environment. The ideal candidate will have expertise in Bayesian Optimisation and a strong background in statistics. A competitive salary, bonuses, and equity options are included.

Leistungen

Competitive salary

Bonus

Share/equity options

Qualifikationen

• Strong understanding of optimisation under uncertainty.
• Expertise in Bayesian Optimisation and Gaussian Processes.
• Hands-on experience with Few-Shot Learning.
• Advanced proficiency in Python and relevant libraries.

Aufgaben

• Collaborate on strategic technical decisions.
• Rapidly prototype and implement AI solutions.
• Develop Bayesian Optimisation algorithms.

Kenntnisse

Optimisation under uncertainty

Bayesian Optimisation

Gaussian Processes

Python proficiency

Statistical analysis

Tools

sklearn

GPflow

Jobbeschreibung

Overview

POSITION SNAPSHOT : Our client is a pioneering AI company dedicated to transforming manufacturing processes through a “small data” approach to AI. In this role, you will be the first dedicated ML Research Engineer, responsible for building core AI capabilities that will directly impact manufacturing efficiency and sustainability at scale. You’ll work in a remote-first, collaborative environment, pioneering practical AI solutions that put an expert operator next to each industrial machine.

Responsibilities

• Collaborate on strategic technical decisions, rapidly prototyping and iterating on AI solutions, and developing and implementing Bayesian Optimisation algorithms that suggest batches of experiments.
• Implement knowledge transfer capabilities across machines and create robust data quality systems.

Qualifications

• Strong understanding of optimisation under uncertainty and multi-objective optimisation, and occasional engagement with manufacturing customers to understand real-world constraints.
• Expertise in Bayesian Optimisation and Gaussian Processes (theory and practical), hands-on experience with Few-Shot Learning, and a solid background in statistics and experimental design.
• Advanced proficiency in Python and libraries like sklearn and GPflow.
• Manufacturing background and a startup mindset that prioritises practical, deployable solutions over theoretical approaches.

Role Details

• Remote role, competitive salary, Bonus, Share / equity options.

Position Snapshot is a summary of the role. Full job description is available from the client