Machine Learning Researcher

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

Zusammenfassung

A pioneering AI company is seeking a dedicated ML Research Engineer to develop AI capabilities to enhance manufacturing efficiency and sustainability. This remote role involves prototyping AI solutions, implementing Bayesian Optimisation algorithms, and engaging with manufacturing clients. Candidates should have strong expertise in optimisation, statistics, and programming. Competitive salary and equity options are offered.

Leistungen

Competitive salary

Bonus

Share / equity options

Qualifikationen

• Strong expertise in Bayesian Optimisation and Gaussian Processes (theory and practical).
• Hands-on experience with Few-Shot Learning.
• Advanced proficiency in Python and libraries like sklearn and GPflow.

Aufgaben

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

Kenntnisse

Optimisation under uncertainty

Bayesian Optimisation

Gaussian Processes

Few-Shot Learning

Python and sklearn

Data quality systems

Jobbeschreibung

Overview

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

• Key duties include collaborating on strategic technical decisions, rapidly prototyping and iterating on AI solutions, and developing and implementing Bayesian Optimisation algorithms that suggest batches of experiments.
• You will also implement knowledge transfer capabilities across machines and create robust data quality systems.

Qualifications

• The role requires a strong understanding of optimisation under uncertainty and multi-objective optimisation, and you will occasionally engage directly with manufacturing customers to understand real-world constraints.
• Candidates should have a strong 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.
• The position requires advanced proficiency in Python and libraries like sklearn and GPflow, a Manufacturing background, along with a startup mindset that prioritises practical, deployable solutions over theoretical approaches. The ideal candidate will be motivated by solving real-world problems with tangible industrial impact and be comfortable with ambiguity and rapid priority changes.

What we offer

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

Additional

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